{"content_id":"ardq66qm2e","slug":"claude-opus-5-verification-and-migration-guide","locale":"en","schema_type":"TechArticle","category":"how_to","category_name":"How-to","title":"How to Audit Prompts and Harnesses Before Switching to Claude Opus 5","summary":"This guide distinguishes the claims made in the provided material about Claude Opus 5 from verifiable information and explains how to redesign prompts, harnesses, and evaluation systems when adopting a new-generation model. Release status, pricing, and model names must be confirmed using Anthropic's official model list and pricing page.","author":{"name":"Injoys Editorial Team","url":"https://injoys.com/ko/about"},"key_points":["The release dates, pricing, and performance claims for Claude Opus 5, Fable 5, and Sonnet 5 in the provided material must be independently verified against official sources before use.","Rather than copying existing prompts unchanged, quality, cost, and latency should be remeasured using real work data when adopting a new model.","Redundant verification instructions and unlimited subagent calls can increase costs and execution time without improving results.","Separating system instructions, project rules, Skills loaded when needed, and technical references makes context easier to manage.","Internal evaluations that reflect an organization's actual work and the cost of failure provide a more direct basis for model selection than benchmark rankings."],"content_markdown":"The provided material introduces Claude Opus 5 as a model designed for everyday enterprise and agent tasks, arguing that existing prompts and harnesses must be redesigned for the new generation of Claude. However, **the release dates, pricing, performance, and partner statements concerning Claude Opus 5, Fable 5, and Sonnet 5 mentioned in the material have not been independently verified based solely on the information provided in this article.** In particular, whether `Fable` is an official Anthropic model name should first be checked against the official model list.\n\nAccordingly, rather than repeating this release information as established fact, this document distinguishes between items that must be confirmed through official documentation and verification procedures that can be applied to an actual model migration.\n\n## Release Information to Verify First\n\nBefore deploying a new model through the API, Claude app, or Claude Code, compare the following items against Anthropic’s official documentation and the model selection screen of the service you use.\n\n| Item to verify | Claim in the provided material | Verification required |\n|---|---|---|\n| Model names | Claude Opus 5, Fable 5, Sonnet 5 | Exact product names and model IDs in the official model list |\n| Release dates | June 9, June 30, and July 24, respectively | Year and date in official announcements and changelogs |\n| Opus 5 pricing | $5 input, $25 output per million tokens | Official API pricing, including separate rates for batch, cache, and long-context usage |\n| Default model in the product | New default model for Claude Max | Availability by region, plan, and client |\n| Roles of each model | Divided into long-running autonomous tasks, everyday tasks, and lightweight tasks | Official model descriptions and results from evaluations on actual work |\n| Performance improvements | Specific percentage improvements over previous models | Evaluation tasks, sample size, measurement criteria, and original partner statements |\n\nIf a model name or price is absent from official documentation, it must not be used for API configuration or budget calculations. If you use a cloud provider or reseller service, its model IDs, pricing, and availability dates may also differ from those of the direct Anthropic API.\n\n## Decision Criteria That Must Change During Model Migration\n\n### 1. Measure Efficiency per Task Rather Than Peak Performance\n\nProcessing every request with the most expensive model can quickly increase costs when an agent repeatedly invokes multiple tools and subagents. Models should be selected by considering the following metrics together, rather than by name or tier.\n\n- **Success rate:** Percentage of tasks that meet requirements without human correction\n- **Total cost:** Cost including retries, tool calls, and subagent calls, not just the initial request\n- **Completion time:** Time including waiting and human review and correction\n- **Cost of failure:** Impact caused by failures such as security defects, incorrect deployments, or omitted analysis\n- **Consistency:** Degree to which results vary when the same type of task is repeated\n\nCost per task should not be judged solely by token pricing. Conceptually, it can be calculated as follows.\n\n`Total cost per task = primary model cost + subagent cost + tool cost + retry cost + human review cost`\n\n### 2. Separate Public Benchmarks From Internal Evaluations\n\nPublic benchmarks are a starting point for comparing models’ general characteristics, but they do not guarantee success with a specific codebase, document format, or set of business rules. Organizations should create internal evaluation sets from anonymized real-world tasks.\n\nA good evaluation set includes the following cases.\n\n- Representative tasks that should be completed successfully\n- Edge cases that models frequently get wrong\n- Tasks with ambiguous requirements that require follow-up questions\n- Tasks that require tool calls or verification against external sources\n- High-risk tasks that require execution to stop or human approval\n- Tasks that require state to be saved and recovered during long-running execution\n\nFor valid comparisons, the same inputs, tools, time limits, and success criteria must be applied to each model. It is safer to record success rates and cost distributions across multiple repetitions than to rely on one or two impressive results.\n\n### 3. Evaluate the Model and Harness as a Single System\n\nA **harness** means the execution environment surrounding a model. It includes the system prompt, project instructions, retrieval, memory, tools, Skills, subagents, permission management, and validation and retry logic.\n\nThe same model can produce different results depending on the harness. For example, if the model writes and runs tests itself while the harness also enforces the same validation, the work may be duplicated. Conversely, it is risky to leave stages requiring deterministic controls, such as deployment approval or security checks, to the model’s autonomous judgment.\n\nThe core principle is to **distinguish between reasoning the model performs well and controls the system must guarantee**.\n\n## Six Things to Review in Prompts and Harnesses\n\n### 1. Experimentally Remove Redundant Validation and Rechecking Instructions\n\nThe right answer is not to delete every sentence such as `You must check your work again after completion.` First, trace whether the validation the new model performs spontaneously overlaps with the harness’s validation stages.\n\n- If the model’s self-review merely repeats itself without improving quality, shorten the prompt.\n- Keep checks that can be automated, such as tests, schema validation, and static analysis, in the harness.\n- Do not replace approval for high-risk tasks such as payments, deployments, or data deletion with the model’s self-validation.\n\n### 2. Define Conditions and Upper Limits for subagent Calls\n\nsubagents are useful for parallel research or separating specialized domains, but delegating even small tasks increases cost and latency. Policies such as the following can be specified.\n\n- Use subagents only for tasks that can be divided independently.\n- Limit how many may run concurrently within a single request.\n- Give each subagent a clear deliverable and termination condition.\n- Prevent multiple agents from redundantly searching the same material.\n- Require human approval when the estimated cost or time exceeds a threshold.\n\n### 3. Replace Detailed Prohibitions With Decision Criteria\n\nLong lists of prohibitions may conflict with one another or fail to address new situations. In low-risk areas such as style, the model can be allowed to read the surrounding context and make a judgment.\n\n- Prescriptive: `Never write a multi-paragraph docstring.`\n- Judgment-based: `Follow the existing code’s comment density, docstring format, naming, and idioms.`\n\nHowever, rules with a high cost of violation, such as those concerning personal data handling, security, or legal obligations, should remain as explicit constraints backed by programmatic checks.\n\n### 4. Specify Response Length and Output Format Directly\n\nThe resources used for reasoning and the length of the answer shown to the user are not the same thing. Even if the client provides an `effort` option or a similar reasoning-intensity setting, specify separate output conditions when a short answer is required.\n\nExamples include the following.\n\n- `State the conclusion first and summarize the rationale in at most three points.`\n- `Keep the final answer within 500 characters.`\n- `Return only a valid JSON object without explanation.`\n- `Report only the changed files, key reasons, and remaining risks.`\n\n### 5. Recalibrate Reasoning Intensity Using Actual Tasks\n\nDo not apply the reasoning-intensity or effort defaults used with a previous model to a new model unchanged. Measure the cost curve by starting with a low setting and increasing it only when quality is insufficient.\n\n| Task type | Initial setting direction | Condition for increasing it |\n|---|---|---|\n| Classification and format conversion | Start low | When schema errors or omissions recur |\n| General document and code edits | Compare middle-range settings | When dependencies across multiple files are missed |\n| Complex debugging | Test medium or higher | When root-cause analysis and validation success rates are insufficient |\n| Long-running agent tasks | Measure by stage | During difficult stages that require replanning or recovery |\n\nThe exact option names and supported scope may vary by API version and product, so official documentation must be checked.\n\n### 6. Divide Context by Role and Disclose It Progressively\n\nPutting every instruction into a single system prompt or `CLAUDE.md` may cause irrelevant information to be included with every request. The following layered structure is practical.\n\n1. **System and product instructions:** Rules that are always required, such as roles, safety boundaries, and output contracts\n2. **Lightweight project instructions:** Build commands, directory structure, and common workflows\n3. **Skills loaded when needed:** Conditional procedures for deployment, database changes, specific frameworks, and similar tasks\n4. **Technical references:** API schemas, code examples, design documents, and testable specifications\n\nThis can be called **progressive disclosure**. Allow the model to retrieve or load the material needed for the current stage, but record which material it used to ensure reproducibility and auditability.\n\n## Recommended Migration Procedure\n\n### Step 1: Lock Down the Current State\n\nSave the existing model’s prompts, tool versions, success rate, token usage, latency, and failure cases. Without a baseline, it is difficult to determine whether the new model has actually improved.\n\n### Step 2: Verify Model Information and Permissions\n\nConfirm the official model ID, pricing, context limit, tool support, and data retention policy. Restrict write, delete, and deployment permissions in the test environment.\n\n### Step 3: Test With the Existing Harness Unchanged\n\nDo not change everything at once initially. Replacing only the model and comparing it with the baseline makes it possible to isolate the impact of the model change.\n\n### Step 4: Remove Redundant Instructions One at a Time\n\nRemove one category at a time, such as validation instructions, verbose style rules, unnecessary examples, or references that are always injected. Measure quality and cost again after each change.\n\n### Step 5: Create a Routing Policy\n\nSelect models according to task difficulty, risk, expected context, and time limits. The model-specific roles proposed in the provided material should be tested as hypotheses after the official names and performance have been confirmed; they should not be adopted unchanged as operational policy.\n\n### Step 6: Deploy to Limited Traffic First\n\nApply the new model first to a subset of users or non-risky tasks. Observe the failure rate, retries, number of subagents, tool errors, and human correction time before expanding deployment.\n\n## Operational Checklist\n\n- [ ] The official model name and API model ID have been confirmed.\n- [ ] The actual applicable rates for input, output, cache, batch, and other usage have been confirmed.\n- [ ] An internal evaluation set composed of actual work exists.\n- [ ] The model’s and harness’s validation stages do not overlap.\n- [ ] Criteria for subagent calls, concurrent execution limits, and budget caps are defined.\n- [ ] Response length and output schema are specified.\n- [ ] Quality, cost, and latency have been compared across reasoning-intensity settings.\n- [ ] Deterministic checks and human approval remain in place for high-risk tasks.\n- [ ] Context is separated into persistent instructions, Skills, and references.\n- [ ] The previous model and settings are ready for rollback.\n\n## Conclusion\n\nThe key to migrating to a new model is not to shorten prompts unconditionally or expand autonomy without limits. The key is to **verify official product information first and then redivide the roles of the model and harness through evaluations based on actual work**.\n\nThe figures and names related to Claude Opus 5 in the provided material should be treated as provisional information until official evidence is confirmed. However, removing redundant validation, limiting subagents, defining clear output contracts, progressively disclosing context, and routing based on internal evaluations are migration principles that can be applied regardless of model generation.","content_html":"\u003cp\u003eThe provided material introduces Claude Opus 5 as a model designed for everyday enterprise and agent tasks, arguing that existing prompts and harnesses must be redesigned for the new generation of Claude. However, \u003cstrong\u003ethe release dates, pricing, performance, and partner statements concerning Claude Opus 5, Fable 5, and Sonnet 5 mentioned in the material have not been independently verified based solely on the information provided in this article.\u003c/strong\u003e In particular, whether \u003ccode\u003eFable\u003c/code\u003e is an official Anthropic model name should first be checked against the official model list.\u003c/p\u003e\n\u003cp\u003eAccordingly, rather than repeating this release information as established fact, this document distinguishes between items that must be confirmed through official documentation and verification procedures that can be applied to an actual model migration.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#release-information-to-verify-first\" class=\"anchor\" id=\"release-information-to-verify-first\"\u003e\u003c/a\u003eRelease Information to Verify First\u003c/h2\u003e\n\u003cp\u003eBefore deploying a new model through the API, Claude app, or Claude Code, compare the following items against Anthropic’s official documentation and the model selection screen of the service you use.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eItem to verify\u003c/th\u003e\n\u003cth\u003eClaim in the provided material\u003c/th\u003e\n\u003cth\u003eVerification required\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to verify\"\u003eModel names\u003c/td\u003e\n\u003ctd data-label=\"Claim in the provided material\"\u003eClaude Opus 5, Fable 5, Sonnet 5\u003c/td\u003e\n\u003ctd data-label=\"Verification required\"\u003eExact product names and model IDs in the official model list\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to verify\"\u003eRelease dates\u003c/td\u003e\n\u003ctd data-label=\"Claim in the provided material\"\u003eJune 9, June 30, and July 24, respectively\u003c/td\u003e\n\u003ctd data-label=\"Verification required\"\u003eYear and date in official announcements and changelogs\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to verify\"\u003eOpus 5 pricing\u003c/td\u003e\n\u003ctd data-label=\"Claim in the provided material\"\u003e$5 input, $25 output per million tokens\u003c/td\u003e\n\u003ctd data-label=\"Verification required\"\u003eOfficial API pricing, including separate rates for batch, cache, and long-context usage\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to verify\"\u003eDefault model in the product\u003c/td\u003e\n\u003ctd data-label=\"Claim in the provided material\"\u003eNew default model for Claude Max\u003c/td\u003e\n\u003ctd data-label=\"Verification required\"\u003eAvailability by region, plan, and client\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to verify\"\u003eRoles of each model\u003c/td\u003e\n\u003ctd data-label=\"Claim in the provided material\"\u003eDivided into long-running autonomous tasks, everyday tasks, and lightweight tasks\u003c/td\u003e\n\u003ctd data-label=\"Verification required\"\u003eOfficial model descriptions and results from evaluations on actual work\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Item to verify\"\u003ePerformance improvements\u003c/td\u003e\n\u003ctd data-label=\"Claim in the provided material\"\u003eSpecific percentage improvements over previous models\u003c/td\u003e\n\u003ctd data-label=\"Verification required\"\u003eEvaluation tasks, sample size, measurement criteria, and original partner statements\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eIf a model name or price is absent from official documentation, it must not be used for API configuration or budget calculations. If you use a cloud provider or reseller service, its model IDs, pricing, and availability dates may also differ from those of the direct Anthropic API.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#decision-criteria-that-must-change-during-model-migration\" class=\"anchor\" id=\"decision-criteria-that-must-change-during-model-migration\"\u003e\u003c/a\u003eDecision Criteria That Must Change During Model Migration\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca href=\"#1-measure-efficiency-per-task-rather-than-peak-performance\" class=\"anchor\" id=\"1-measure-efficiency-per-task-rather-than-peak-performance\"\u003e\u003c/a\u003e1. Measure Efficiency per Task Rather Than Peak Performance\u003c/h3\u003e\n\u003cp\u003eProcessing every request with the most expensive model can quickly increase costs when an agent repeatedly invokes multiple tools and subagents. Models should be selected by considering the following metrics together, rather than by name or tier.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003eSuccess rate:\u003c/strong\u003e Percentage of tasks that meet requirements without human correction\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTotal cost:\u003c/strong\u003e Cost including retries, tool calls, and subagent calls, not just the initial request\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCompletion time:\u003c/strong\u003e Time including waiting and human review and correction\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eCost of failure:\u003c/strong\u003e Impact caused by failures such as security defects, incorrect deployments, or omitted analysis\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eConsistency:\u003c/strong\u003e Degree to which results vary when the same type of task is repeated\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCost per task should not be judged solely by token pricing. Conceptually, it can be calculated as follows.\u003c/p\u003e\n\u003cp\u003e\u003ccode\u003eTotal cost per task = primary model cost + subagent cost + tool cost + retry cost + human review cost\u003c/code\u003e\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#2-separate-public-benchmarks-from-internal-evaluations\" class=\"anchor\" id=\"2-separate-public-benchmarks-from-internal-evaluations\"\u003e\u003c/a\u003e2. Separate Public Benchmarks From Internal Evaluations\u003c/h3\u003e\n\u003cp\u003ePublic benchmarks are a starting point for comparing models’ general characteristics, but they do not guarantee success with a specific codebase, document format, or set of business rules. Organizations should create internal evaluation sets from anonymized real-world tasks.\u003c/p\u003e\n\u003cp\u003eA good evaluation set includes the following cases.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRepresentative tasks that should be completed successfully\u003c/li\u003e\n\u003cli\u003eEdge cases that models frequently get wrong\u003c/li\u003e\n\u003cli\u003eTasks with ambiguous requirements that require follow-up questions\u003c/li\u003e\n\u003cli\u003eTasks that require tool calls or verification against external sources\u003c/li\u003e\n\u003cli\u003eHigh-risk tasks that require execution to stop or human approval\u003c/li\u003e\n\u003cli\u003eTasks that require state to be saved and recovered during long-running execution\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFor valid comparisons, the same inputs, tools, time limits, and success criteria must be applied to each model. It is safer to record success rates and cost distributions across multiple repetitions than to rely on one or two impressive results.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#3-evaluate-the-model-and-harness-as-a-single-system\" class=\"anchor\" id=\"3-evaluate-the-model-and-harness-as-a-single-system\"\u003e\u003c/a\u003e3. Evaluate the Model and Harness as a Single System\u003c/h3\u003e\n\u003cp\u003eA \u003cstrong\u003eharness\u003c/strong\u003e means the execution environment surrounding a model. It includes the system prompt, project instructions, retrieval, memory, tools, Skills, subagents, permission management, and validation and retry logic.\u003c/p\u003e\n\u003cp\u003eThe same model can produce different results depending on the harness. For example, if the model writes and runs tests itself while the harness also enforces the same validation, the work may be duplicated. Conversely, it is risky to leave stages requiring deterministic controls, such as deployment approval or security checks, to the model’s autonomous judgment.\u003c/p\u003e\n\u003cp\u003eThe core principle is to \u003cstrong\u003edistinguish between reasoning the model performs well and controls the system must guarantee\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#six-things-to-review-in-prompts-and-harnesses\" class=\"anchor\" id=\"six-things-to-review-in-prompts-and-harnesses\"\u003e\u003c/a\u003eSix Things to Review in Prompts and Harnesses\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca href=\"#1-experimentally-remove-redundant-validation-and-rechecking-instructions\" class=\"anchor\" id=\"1-experimentally-remove-redundant-validation-and-rechecking-instructions\"\u003e\u003c/a\u003e1. Experimentally Remove Redundant Validation and Rechecking Instructions\u003c/h3\u003e\n\u003cp\u003eThe right answer is not to delete every sentence such as \u003ccode\u003eYou must check your work again after completion.\u003c/code\u003e First, trace whether the validation the new model performs spontaneously overlaps with the harness’s validation stages.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIf the model’s self-review merely repeats itself without improving quality, shorten the prompt.\u003c/li\u003e\n\u003cli\u003eKeep checks that can be automated, such as tests, schema validation, and static analysis, in the harness.\u003c/li\u003e\n\u003cli\u003eDo not replace approval for high-risk tasks such as payments, deployments, or data deletion with the model’s self-validation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca href=\"#2-define-conditions-and-upper-limits-for-subagent-calls\" class=\"anchor\" id=\"2-define-conditions-and-upper-limits-for-subagent-calls\"\u003e\u003c/a\u003e2. Define Conditions and Upper Limits for subagent Calls\u003c/h3\u003e\n\u003cp\u003esubagents are useful for parallel research or separating specialized domains, but delegating even small tasks increases cost and latency. Policies such as the following can be specified.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUse subagents only for tasks that can be divided independently.\u003c/li\u003e\n\u003cli\u003eLimit how many may run concurrently within a single request.\u003c/li\u003e\n\u003cli\u003eGive each subagent a clear deliverable and termination condition.\u003c/li\u003e\n\u003cli\u003ePrevent multiple agents from redundantly searching the same material.\u003c/li\u003e\n\u003cli\u003eRequire human approval when the estimated cost or time exceeds a threshold.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca href=\"#3-replace-detailed-prohibitions-with-decision-criteria\" class=\"anchor\" id=\"3-replace-detailed-prohibitions-with-decision-criteria\"\u003e\u003c/a\u003e3. Replace Detailed Prohibitions With Decision Criteria\u003c/h3\u003e\n\u003cp\u003eLong lists of prohibitions may conflict with one another or fail to address new situations. In low-risk areas such as style, the model can be allowed to read the surrounding context and make a judgment.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePrescriptive: \u003ccode\u003eNever write a multi-paragraph docstring.\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eJudgment-based: \u003ccode\u003eFollow the existing code’s comment density, docstring format, naming, and idioms.\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eHowever, rules with a high cost of violation, such as those concerning personal data handling, security, or legal obligations, should remain as explicit constraints backed by programmatic checks.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#4-specify-response-length-and-output-format-directly\" class=\"anchor\" id=\"4-specify-response-length-and-output-format-directly\"\u003e\u003c/a\u003e4. Specify Response Length and Output Format Directly\u003c/h3\u003e\n\u003cp\u003eThe resources used for reasoning and the length of the answer shown to the user are not the same thing. Even if the client provides an \u003ccode\u003eeffort\u003c/code\u003e option or a similar reasoning-intensity setting, specify separate output conditions when a short answer is required.\u003c/p\u003e\n\u003cp\u003eExamples include the following.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ccode\u003eState the conclusion first and summarize the rationale in at most three points.\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eKeep the final answer within 500 characters.\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eReturn only a valid JSON object without explanation.\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003eReport only the changed files, key reasons, and remaining risks.\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch3\u003e\n\u003ca href=\"#5-recalibrate-reasoning-intensity-using-actual-tasks\" class=\"anchor\" id=\"5-recalibrate-reasoning-intensity-using-actual-tasks\"\u003e\u003c/a\u003e5. Recalibrate Reasoning Intensity Using Actual Tasks\u003c/h3\u003e\n\u003cp\u003eDo not apply the reasoning-intensity or effort defaults used with a previous model to a new model unchanged. Measure the cost curve by starting with a low setting and increasing it only when quality is insufficient.\u003c/p\u003e\n\u003cdiv class=\"overflow-x-auto\"\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eTask type\u003c/th\u003e\n\u003cth\u003eInitial setting direction\u003c/th\u003e\n\u003cth\u003eCondition for increasing it\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Task type\"\u003eClassification and format conversion\u003c/td\u003e\n\u003ctd data-label=\"Initial setting direction\"\u003eStart low\u003c/td\u003e\n\u003ctd data-label=\"Condition for increasing it\"\u003eWhen schema errors or omissions recur\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Task type\"\u003eGeneral document and code edits\u003c/td\u003e\n\u003ctd data-label=\"Initial setting direction\"\u003eCompare middle-range settings\u003c/td\u003e\n\u003ctd data-label=\"Condition for increasing it\"\u003eWhen dependencies across multiple files are missed\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Task type\"\u003eComplex debugging\u003c/td\u003e\n\u003ctd data-label=\"Initial setting direction\"\u003eTest medium or higher\u003c/td\u003e\n\u003ctd data-label=\"Condition for increasing it\"\u003eWhen root-cause analysis and validation success rates are insufficient\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd data-label=\"Task type\"\u003eLong-running agent tasks\u003c/td\u003e\n\u003ctd data-label=\"Initial setting direction\"\u003eMeasure by stage\u003c/td\u003e\n\u003ctd data-label=\"Condition for increasing it\"\u003eDuring difficult stages that require replanning or recovery\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/div\u003e\n\u003cp\u003eThe exact option names and supported scope may vary by API version and product, so official documentation must be checked.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#6-divide-context-by-role-and-disclose-it-progressively\" class=\"anchor\" id=\"6-divide-context-by-role-and-disclose-it-progressively\"\u003e\u003c/a\u003e6. Divide Context by Role and Disclose It Progressively\u003c/h3\u003e\n\u003cp\u003ePutting every instruction into a single system prompt or \u003ccode\u003eCLAUDE.md\u003c/code\u003e may cause irrelevant information to be included with every request. The following layered structure is practical.\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cstrong\u003eSystem and product instructions:\u003c/strong\u003e Rules that are always required, such as roles, safety boundaries, and output contracts\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eLightweight project instructions:\u003c/strong\u003e Build commands, directory structure, and common workflows\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eSkills loaded when needed:\u003c/strong\u003e Conditional procedures for deployment, database changes, specific frameworks, and similar tasks\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003eTechnical references:\u003c/strong\u003e API schemas, code examples, design documents, and testable specifications\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis can be called \u003cstrong\u003eprogressive disclosure\u003c/strong\u003e. Allow the model to retrieve or load the material needed for the current stage, but record which material it used to ensure reproducibility and auditability.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#recommended-migration-procedure\" class=\"anchor\" id=\"recommended-migration-procedure\"\u003e\u003c/a\u003eRecommended Migration Procedure\u003c/h2\u003e\n\u003ch3\u003e\n\u003ca href=\"#step-1-lock-down-the-current-state\" class=\"anchor\" id=\"step-1-lock-down-the-current-state\"\u003e\u003c/a\u003eStep 1: Lock Down the Current State\u003c/h3\u003e\n\u003cp\u003eSave the existing model’s prompts, tool versions, success rate, token usage, latency, and failure cases. Without a baseline, it is difficult to determine whether the new model has actually improved.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#step-2-verify-model-information-and-permissions\" class=\"anchor\" id=\"step-2-verify-model-information-and-permissions\"\u003e\u003c/a\u003eStep 2: Verify Model Information and Permissions\u003c/h3\u003e\n\u003cp\u003eConfirm the official model ID, pricing, context limit, tool support, and data retention policy. Restrict write, delete, and deployment permissions in the test environment.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#step-3-test-with-the-existing-harness-unchanged\" class=\"anchor\" id=\"step-3-test-with-the-existing-harness-unchanged\"\u003e\u003c/a\u003eStep 3: Test With the Existing Harness Unchanged\u003c/h3\u003e\n\u003cp\u003eDo not change everything at once initially. Replacing only the model and comparing it with the baseline makes it possible to isolate the impact of the model change.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#step-4-remove-redundant-instructions-one-at-a-time\" class=\"anchor\" id=\"step-4-remove-redundant-instructions-one-at-a-time\"\u003e\u003c/a\u003eStep 4: Remove Redundant Instructions One at a Time\u003c/h3\u003e\n\u003cp\u003eRemove one category at a time, such as validation instructions, verbose style rules, unnecessary examples, or references that are always injected. Measure quality and cost again after each change.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#step-5-create-a-routing-policy\" class=\"anchor\" id=\"step-5-create-a-routing-policy\"\u003e\u003c/a\u003eStep 5: Create a Routing Policy\u003c/h3\u003e\n\u003cp\u003eSelect models according to task difficulty, risk, expected context, and time limits. The model-specific roles proposed in the provided material should be tested as hypotheses after the official names and performance have been confirmed; they should not be adopted unchanged as operational policy.\u003c/p\u003e\n\u003ch3\u003e\n\u003ca href=\"#step-6-deploy-to-limited-traffic-first\" class=\"anchor\" id=\"step-6-deploy-to-limited-traffic-first\"\u003e\u003c/a\u003eStep 6: Deploy to Limited Traffic First\u003c/h3\u003e\n\u003cp\u003eApply the new model first to a subset of users or non-risky tasks. Observe the failure rate, retries, number of subagents, tool errors, and human correction time before expanding deployment.\u003c/p\u003e\n\u003ch2\u003e\n\u003ca href=\"#operational-checklist\" class=\"anchor\" id=\"operational-checklist\"\u003e\u003c/a\u003eOperational Checklist\u003c/h2\u003e\n\u003cul\u003e\n\u003cli\u003e The official model name and API model ID have been confirmed.\u003c/li\u003e\n\u003cli\u003e The actual applicable rates for input, output, cache, batch, and other usage have been confirmed.\u003c/li\u003e\n\u003cli\u003e An internal evaluation set composed of actual work exists.\u003c/li\u003e\n\u003cli\u003e The model’s and harness’s validation stages do not overlap.\u003c/li\u003e\n\u003cli\u003e Criteria for subagent calls, concurrent execution limits, and budget caps are defined.\u003c/li\u003e\n\u003cli\u003e Response length and output schema are specified.\u003c/li\u003e\n\u003cli\u003e Quality, cost, and latency have been compared across reasoning-intensity settings.\u003c/li\u003e\n\u003cli\u003e Deterministic checks and human approval remain in place for high-risk tasks.\u003c/li\u003e\n\u003cli\u003e Context is separated into persistent instructions, Skills, and references.\u003c/li\u003e\n\u003cli\u003e The previous model and settings are ready for rollback.\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\n\u003ca href=\"#conclusion\" class=\"anchor\" id=\"conclusion\"\u003e\u003c/a\u003eConclusion\u003c/h2\u003e\n\u003cp\u003eThe key to migrating to a new model is not to shorten prompts unconditionally or expand autonomy without limits. The key is to \u003cstrong\u003everify official product information first and then redivide the roles of the model and harness through evaluations based on actual work\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe figures and names related to Claude Opus 5 in the provided material should be treated as provisional information until official evidence is confirmed. However, removing redundant validation, limiting subagents, defining clear output contracts, progressively disclosing context, and routing based on internal evaluations are migration principles that can be applied regardless of model generation.\u003c/p\u003e\n","tags":["Prompt Engineering","AI Agents","Anthropic","Claude","Model Evaluation"],"faqs":[{"question":"Has Claude Opus 5 been officially released?","answer":"The provided materials include a release date and pricing, but this has not been independently verified based solely on the information given in this article. It is safest not to treat it as confirmed product information until the exact model name and model ID are verified in Anthropic's official model list, announcements, and API console."},{"question":"Is Fable 5 an official Anthropic model name?","answer":"This cannot be confirmed from the provided materials alone. Since Anthropic may use different API model IDs or service-specific labels even when product names are similar, it is necessary to verify whether the name `Fable 5` actually exists in the official model list."},{"question":"Do I need to delete all existing prompts when switching to a new Claude model?","answer":"No. First, conduct a baseline evaluation using the existing configuration, then compare quality and cost while removing redundant verification instructions or unnecessary style rules one at a time. Controls that must be guaranteed by the system, such as security checks, output schema validation, and deployment approval, should be retained."},{"question":"What is a harness?","answer":"A harness is the system surrounding an AI model that enables it to perform real-world work. It includes system prompts, project instructions, tools, search, memory, Skills, subagents, retries, permission management, and automated validation procedures."},{"question":"How should subagent usage be limited?","answer":"Use subagents only for tasks that can be divided independently, and set upper limits on the number of concurrent executions and total calls. Specify each subagent's deliverables and termination conditions, and design the system to require human approval if the expected cost or time exceeds a threshold."},{"question":"Why are internal evaluations more important than public benchmarks?","answer":"Public benchmarks do not fully reflect an organization's codebase, document formats, tool environment, or the cost of failure. Success rate, total cost, completion time, and consistency of results must be measured using real work cases to determine which model is suitable for the operational environment."},{"question":"Can tests be eliminated if the model has self-validation?","answer":"No. A model's self-review is a supplementary measure and does not replace testing, schema checks, static analysis, or security policies. High-risk tasks such as deployment, payments, and data deletion require deterministic checks and human approval."},{"question":"If I lower effort, will the response automatically become shorter?","answer":"Not necessarily. Reasoning intensity and final output length may be controlled separately. If a concise answer is needed, the response format, such as the character count, number of items, or output schema, should be specified directly in the prompt."}],"sources":[{"url":"https://docs.anthropic.com/en/docs/about-claude/models/overview","title":"Anthropic Docs: Models overview","type":"source"},{"url":"https://www.anthropic.com/pricing","title":"Anthropic Pricing","type":"data_point"},{"url":"https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview","title":"Anthropic Docs: Prompt engineering overview","type":"source"},{"url":"https://docs.anthropic.com/en/docs/claude-code/memory","title":"Anthropic Docs: Claude Code memory","type":"source"}],"images":[{"id":324,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6MzY5MCwicHVyIjoiYmxvYl9pZCJ9fQ==--f44d725b558668593419631e29f28834deb66ecc/ai-95ae89bc.webp","is_representative":true,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"체크 항목과 경고 장벽 사이에서 AI 큐브를 돋보기로 점검하는 일러스트","caption":"모델 전환 전 프롬프트와 하네스의 성능, 보안, 비용을 점검하는 과정을 나타낸다.","description":null},"en":{"alt":"Illustration of AI cubes being inspected beside a warning barrier and checklist icons","caption":"The scene represents checking prompts, harnesses, performance, security, and cost before a model switch.","description":null},"ja":{"alt":"警告バリケードと確認項目のそばでAIキューブを虫眼鏡で点検するイラスト","caption":"モデル移行前にプロンプトやハーネスの性能、安全性、コストを確認する工程を表している。","description":null},"es":{"alt":"Ilustración de cubos de IA inspeccionados junto a una barrera de alerta e iconos de control","caption":"La escena representa la revisión de prompts, arneses, rendimiento, seguridad y costes antes de cambiar de modelo.","description":null},"id":{"alt":"Ilustrasi kubus AI yang diperiksa di dekat penghalang peringatan dan ikon daftar cek","caption":"Adegan ini menggambarkan pemeriksaan prompt, harness, kinerja, keamanan, dan biaya sebelum beralih model.","description":null},"pt":{"alt":"Ilustração de cubos de IA inspecionados junto a uma barreira de alerta e ícones de verificação","caption":"A cena representa a revisão de prompts, harnesses, desempenho, segurança e custos antes da troca de modelo.","description":null},"zh-hant":{"alt":"在警示柵欄與檢查圖示旁以放大鏡檢視 AI 方塊的插圖","caption":"此圖呈現模型切換前檢查提示詞、工具框架、效能、安全性與成本的流程。","description":null}}},{"id":325,"url":"https://injoys.com/rails/active_storage/blobs/proxy/eyJfcmFpbHMiOnsiZGF0YSI6MzY5NiwicHVyIjoiYmxvYl9pZCJ9fQ==--5ddddd2dfbbbcedb1849fbdfe606aca94f9a98af/ai-b298a672.webp","is_representative":false,"generation_method":"ai_image","license":"ai_generated","mime_type":"image/webp","translations":{"ko":{"alt":"중앙 AI 모델에 보안, 문서, 사용자, 도구와 에이전트 차단 장치가 연결된 점검 구성도","caption":"모델 전환 전 프롬프트와 에이전트 하네스의 연결, 안전장치, 평가 항목을 점검하는 흐름을 나타낸다.","description":null},"en":{"alt":"AI model linked to security, documents, users, tools, agent controls, and evaluation indicators","caption":"The diagram contextualizes checks for prompts, agent harnesses, safeguards, and evaluations before a model switch.","description":null},"ja":{"alt":"中央のAIモデルにセキュリティ、文書、ユーザー、ツール、エージェント制御が接続された構成図","caption":"モデル移行前にプロンプトやエージェントハーネス、安全策、評価項目を確認する流れを示している。","description":null},"es":{"alt":"Modelo de IA conectado con seguridad, documentos, usuarios, herramientas, controles de agentes e indicadores","caption":"El diagrama representa la revisión de prompts, arneses de agentes, salvaguardas y evaluaciones antes de cambiar de modelo.","description":null},"id":{"alt":"Model AI terhubung ke keamanan, dokumen, pengguna, alat, kontrol agen, dan indikator evaluasi","caption":"Diagram ini menggambarkan pemeriksaan prompt, harness agen, pengaman, dan evaluasi sebelum pergantian model.","description":null},"pt":{"alt":"Modelo de IA ligado a segurança, documentos, usuários, ferramentas, controles de agentes e indicadores","caption":"O diagrama representa a verificação de prompts, harnesses de agentes, proteções e avaliações antes da troca de modelo.","description":null},"zh-hant":{"alt":"中央 AI 模型連接安全、文件、使用者、工具、代理控制與評估指標的架構圖","caption":"此圖呈現模型切換前對提示詞、代理框架、安全機制與評估項目的檢查流程。","description":null}}}],"published_at":"2026-07-28T11:42:11+09:00","updated_at":"2026-07-28T11:42:11+09:00","license":"cc_by","translation_status":"reviewed","available_locales":["ko","en","ja","es"],"data_locales":["ko","en","ja","es","id","pt","zh-hant"],"url":"https://injoys.com/en/articles/claude-opus-5-verification-and-migration-guide"}