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Anthropic CCAR-F Sample Questions – Free Practice Test & Real Exam Prep
Question #1
You are integrating Claude Code into your Continuous Integration/Continuous Deployment(CI/CD) pipeline. The system runs automated code reviews, generates test cases, and providesfeedback on pull requests. You need to design prompts that provide actionable feedback andminimize false positives.The automated review consistently flags patterns your team uses intentionally—forceunwrapping optionals in test files, using large coordinator classes that follow your establishedarchitecture, and importing internally maintained modules marked as deprecated in the publicSDK. Developers dismiss approximately 30% of all findings as project-specific false positives.Which approach prevents the model from generating these findings in the first place bysupplying the project’s conventions as persistent context during every review?
A. Document the team’s accepted patterns and intentional conventions in the project’sCLAUDE.md file so the model receives this context during every review.
B. Configure the review to analyze only the changed lines in the diff without the surroundingfile context, reducing the amount of code the model evaluates.
C. Build post-processing keyword filters that suppress findings containing terms such as“force unwrap,” “large class,” or “deprecated import” before results reach developers.
D. Havedevelopers add inline suppression comments at flagged lines and preprocess diffs toexclude suppressed lines before sending code to the model.
Answer: A EXPERT VERIFICATION The original answer was correct.
The original answer was correct; CLAUDE.md is the documented mechanism for supplying
persistent project context to every Claude Code invocation, which is exactly what the question
asks for.
EXPLANATION
A thirty percent false-positive rate is the classic symptom of a reviewer that lacks project
context rather than one that lacks capability. Force-unwrapping in tests, large coordinator
classes, and imports of internally maintained modules are all genuinely suspicious in the
abstract; they are only acceptable because this team has decided they are. CLAUDE.md is the
file Claude Code loads automatically at the start of every session in a project, and it is
designed to carry exactly this kind of durable, repository-specific knowledge: architecture
decisions, naming conventions, patterns that look wrong but are intentional, and things that
are handled elsewhere in the toolchain. Because it is loaded before the model reasons about
the diff, it changes what findings are generated rather than filtering them afterwards, which is
what the question stem specifically requires. It also lives in version control, so the conventions
are reviewed, evolve with the codebase, and apply identically to local sessions and CI runs. In
practice the most effective entries are short, concrete, and explain the rationale, for example
stating that test targets deliberately force-unwrap because a nil value should fail the test
loudly. Teams should treat the file as a living artifact and add an entry each time a false
positive is dismissed, which drives the noise rate down over successive releases. A common
misconception is that keyword suppression achieves the same outcome; filtering strings after
the fact discards genuine findings that happen to use the same vocabulary and does nothing
to stop the model wasting reasoning on patterns it should never have flagged.
KEY TAKEAWAYS
? CLAUDE.md is auto-loaded project context and shapes findings before they are generated
? Post-hoc keyword filters suppress real bugs that share vocabulary with false positives
? Record the rationale for intentional patterns, not just the pattern itself
? Feed dismissed false positives back into CLAUDE.md so review noise declines over time
Question #2
After deploying the automated review, you notice high precision but low recall—real bugs areslipping through undetected. Investigation reveals that your review prompt instructs Claude to“only report high-confidence issues you are certain about” and “err on the side of notcommenting.” Developers appreciate the low noise, but a race condition that caused aproduction outage was visible in a reviewed pull request and went unreported. You need tosubstantially improve bug detection while keeping false-positive rates manageable. What isthe most effective approach?
A. Adddetailed few-shot examples demonstrating bug categories Claude should flag—raceconditions, null dereferences, and error-handling gaps—while retaining the high-confidencefiltering instruction.
B. Remove the conservative instructions and have Claude report every potential issue, thenapply a programmatic filter that deduplicates findings and suppresses historically noisycategories.
C. Split the review into a finding stage whose objective is comprehensive coverage—reporting every potential issue with confidence and severity metadata—and a separatestage that verifies and thresholds those findings.
D. Expand the context to include related tests, recent Git history, and the module’sdependency graph so Claude has richer evidence for judging severity.
Answer: C
EXPERT VERIFICATION The original answer was correct.
The original answer was correct: only option C changes the structural trade-off between
precision and recall rather than tuning a single conflicted prompt.
EXPLANATION
The core problem is that one prompt is being asked to do two contradictory jobs at once- find
everything, and only speak when certain. Whenever a single model call carries both a recall
objective and a precision objective, the more restrictive instruction dominates, which is exactly
why the review has high precision and misses a visible race condition. The architectural fix is
to decompose the task into stages with separate, non-conflicting objectives. A finding stage is
instructed to be exhaustive, emitting every candidate issue along with structured metadata
such as confidence, severity, category, and the specific lines of evidence. A second verification
stage then receives each candidate independently and acts as a skeptical reviewer, confirming
or rejecting it against the code, after which a deterministic threshold decides what is actually
posted to the pull request. This is the same generate-then-verify pattern Anthropic uses in its
own code review and security review tooling, and it works because verification is a much
easier, better-bounded judgement than open-ended discovery. It also gives you two
independent tuning knobs: you raise recall by loosening the finder and control noise by
tightening the verifier or the posting threshold, without regressing the other. Operationally, the
metadata makes the pipeline measurable- you can track detected-but-suppressed issues,
compute recall against a labelled bug set, and adjust thresholds per repository or per severity.
A common misconception is that better prompting alone can resolve the tension; in practice,
telling one call to be both comprehensive and conservative reliably collapses toward silence. KEY TAKEAWAYS
? Precision and recall objectives conflict inside a single prompt; separate them into distinct
stages.
? Have the finder emit confidence and severity metadata so filtering becomes deterministic
and tunable.
? Generate-then-verify is the standard pattern for automated code review at scale.
? Structured findings make recall measurable rather than invisible.
Question #3
You are integrating Claude Code into your Continuous Integration/Continuous Deployment(CI/CD) pipeline. The system runs automated code reviews, generates test cases, and providesfeedback on pull requests. You need to design prompts that provide actionable feedback andminimize false positives.After deploying automated code review, developers report that approximately 35% of findingsare false positives following consistent patterns: style suggestions that contradict teamconventions, security warnings for patterns that are safe in the deployment environment, andperformance suggestions that would degrade this particular use case.You want to reduce false positives while enabling the model to generalize its judgment tonovel code patterns it has not seen before.Which approach is most effective?
A. Create a comprehensive specification of every pattern that must not be flagged andinclude the complete document in the system prompt.
B. Include few-shot examples containing annotated code snippets that distinguish acceptableproject patterns from genuine issues in each category.
C. Usekeyword-based post-processing to remove findings containing terms such as“convention,” “context-dependent,” or “trade-off.”
D. Addgeneral instructions telling Claude to be conservative and report only definite issues.
Answer: B EXPERT VERIFICATION The original answer was correct.
The original answer was correct: the requirement to generalise to unseen code patterns is what
selects few-shot examples over an exhaustive enumeration of forbidden findings.
EXPLANATION
The scenario has two constraints that must both be satisfied: cut a thirty-five percent false
positive rate, and have the model apply the same judgement to novel code it has not
encountered. Few-shot examples satisfy both because they teach the underlying decision
boundary rather than a lookup table. By showing annotated pairs in each problem category, for
instance a formatting choice that matches team convention next to a genuine readability
defect, a pattern that is safe because of a deployment-environment guarantee next to a real
injection risk, and an optimisation that would hurt this workload next to a true performance
bug, you give Claude the reasoning principles behind your team's standards. The model then
extrapolates those principles to code it has never seen, which is exactly the generalisation an
exhaustive specification cannot provide, since any enumeration covers only listed patterns,
grows without bound, consumes a large stable prefix of the prompt, and goes stale as the
codebase evolves. Implementation notes matter in practice: keep the example set small and
high-contrast, draw the examples from real dismissed findings so they reflect your actual
disagreements, place them in the stable portion of the prompt and use prompt caching so the
added tokens are inexpensive across many pull requests, and maintain a labelled eval set to
confirm that false positives fall without suppressing true positives. Vague instructions to be
conservative typically trade false positives for missed defects, and keyword filtering on output
text is brittle and can delete valid findings, so neither addresses calibration. KEY TAKEAWAYS
? Few-shot examples teach a decision boundary that generalises to unseen patterns.
? Contrast pairs, acceptable versus genuine issue, are more instructive than one-sided
examples.
? Source examples from real dismissed findings and cache the stable prompt prefix.
? Track false positives and true positives together so noise reduction does not hide real bugs.
Question #4
You are integrating Claude Code into your Continuous Integration/Continuous Deployment(CI/CD) pipeline. The system runs automated code reviews, generates test cases, and providesfeedback on pull requests. You need to design prompts that provide actionable feedback andminimize false positives.Your pipeline reviews every pull request using a single API call with a static prompt containingthe diff and the full text of each changed file. Unchanged files are not included. Developersreport that reviews consistently miss cross-file bugs—for example, a pull request renames afunction’s parameters, but the review does not identify callers in unchanged files that still usethe old argument order.Evaluation shows that cross-file bugs account for 35% of production incidents originating fromreviewed pull requests.What is the most effective change to the review design?
A. Build a static dependency graph and include every file located within two dependencyhops of a changed file.
B. Add instructions asking the model to list external references and reason step by stepabout how each change could affect unseen callers.
C. Redesign the review as a turn-limited agentic task that can read files and search therepository, following references to verify cross-file findings.
D. Runseparate review passes for each changed file with its direct dependants, and thenaggregate and deduplicate the findings through a final consolidation pass.
Answer: C EXPERT VERIFICATION The original answer was correct.
The original answer was correct: the review fails because it is a single static call that cannot see
unchanged callers, and only an agentic review that can search and read the repository resolves
that structurally. EXPLANATION
The root cause is that the review's context is fixed at request-construction time and
deliberately excludes unchanged files, yet cross-file bugs by definition live in unchanged files.
No prompt improvement can conjure code the model was never shown. Converting the review
into a bounded agentic task changes the information model: Claude receives the diff plus tools
such as Grep, Glob, and Read, and can act on what it discovers, searching for every call site of
the renamed function, opening those files, and confirming whether the argument order
actually breaks. This is exactly the workload Claude Code is designed for, and running it in CI
through the GitHub Action or the Agent SDK is a standard enterprise pattern. Two design
constraints keep it practical. Turn limits and a token budget bound cost and latency, which
matters because agentic reviews are more expensive than one static call. Requiring the model
to verify each cross-file finding by citing the specific file and line it read sharply reduces false
positives, since the model must ground the claim in retrieved evidence rather than speculate.
Prompt caching over the stable system prompt and review instructions further reduces cost
across many pull requests. The economics are compelling here because cross-file defects
account for thirty-five percent of production incidents from reviewed pull requests, so the
marginal cost per review is small relative to the incidents avoided. The common misconception
is that telling the model to reason step by step about unseen callers helps; reasoning without
retrieval produces confident guesses, not verified findings. KEY TAKEAWAYS ? Static single-call reviews cannot detect defects in files that were never placed in context.
? Agentic review with Read, Grep, and Glob lets Claude follow references and verify findings.
? Bound agentic reviews with turn limits and token budgets, and cache the stable prompt
prefix.
? Requiring cited file and line evidence for each finding is the main lever against false
positives.
Question #5
The automated review consistently flags patterns your team uses intentionally—forceunwrapping optionals in test files, using large coordinator classes that follow your establishedarchitecture, and importing internally maintained modules marked as deprecated in the publicSDK. Developers are dismissing approximately 30% of all findings as project-specific falsepositives. Which approach prevents the model from generating these findings in the first placeby supplying the project’s conventions as persistent context during every review?
A. Build post-processing keyword filters that suppress findings containing terms such as“force unwrap,” “large class,” or “deprecated import” before results reach developers.
B. Configure the review to analyze only the changed lines in the diff without surrounding filecontext, reducing the amount of code the model evaluates during each review.
C. Have developers add inline suppression comments at flagged lines and preprocess diffs toexclude suppressed lines before sending code to the model.
D. Document the team’s accepted patterns and intentional conventions in the project’sCLAUDE.md file so the model receives this context during every review.
Answer: D EXPERT VERIFICATION The original answer was correct.
The original answer was correct: the question asks specifically for persistent context supplied
during every review, which is the defining role of CLAUDE.md.
EXPLANATION
The false positives here are not model errors in the abstract; each flagged pattern would be a
legitimate finding in a generic codebase and is only acceptable because of decisions this team
has made deliberately. Force-unwrapping in test files, large coordinator classes mandated by
the established architecture, and imports of internally maintained modules that the public SDK
marks deprecated are all project knowledge that the model has no way to infer from a diff.
CLAUDE.md is the mechanism for supplying exactly that knowledge, because Claude Code
loads it automatically for every session in the repository, making it persistent, version
controlled, reviewable, and shared across the whole team rather than living in one engineer's
ad-hoc prompt. Documenting each accepted pattern together with a short rationale and its
scope, for example noting that force-unwrapping is acceptable in test targets but not in
production code, prevents the findings from being generated at all, which is what the question
asks for and which is strictly better than suppressing them after the fact. Practical guidance:
keep entries concrete and brief since the file consumes context on every run, use directory
scoped CLAUDE.md files for module-specific conventions, review the file through normal pull
request process, and revisit it as conventions change so it does not silently mask patterns that
are no longer acceptable. A common misconception is that keyword-based post-filters achieve
the same outcome; they operate on finding text rather than intent, so they inevitably suppress
genuine defects that happen to use the same vocabulary while doing nothing to improve the
model's judgement. KEY TAKEAWAYS
? CLAUDE.md supplies persistent, automatically loaded project context on every Claude Code
review.
? Document intentional conventions with rationale and scope so findings are never generated,
not merely filtered.
? Version-controlled project memory is shared and reviewable, unlike ad-hoc per-developer
prompts.
? Keyword post-filters match text rather than intent and will suppress genuine defects along
with noise.
Question #6
You are integrating Claude Code into your Continuous Integration/Continuous Deployment(CI/CD) pipeline. The system runs automated code reviews, generates test cases, and providesfeedback on pull requests. You need to design prompts that provide actionable feedback andminimize false positives.Your automated reviewer uses a single prompt covering security issues, API design, andbusiness-logic correctness. Your evaluation suite shows strong recall for API-design findings at82% but poor recall for business-logic edge cases in quiz scoring at 34%. When you add fewshot examples of logic bugs to the prompt, logic recall improves to 41%, but API-design recalldrops to 68%.How should you address this trade-off to improve detection across both categories?
A. Split the review into separate focused prompts—one for security and API design andanother for business logic—each with dedicated examples, and then consolidate thefindings before posting.
B. Replace the few-shot examples with a detailed checklist of specific logic edge cases toverify, such as division by zero in score calculations and boundary conditions in gradingthresholds.
C. Upgrade to a more capable model tier because its stronger reasoning will handle bothconcern types in one prompt and eliminate the recall trade-off.
D. Provide the full repository as context instead of only the changed files and surroundingcode, giving the model deeper visibility into business-logic patterns.
Answer: A EXPERT VERIFICATION The original answer was correct.
The original answer was correct: the observed inverse movement in recall between categories
is the classic signature of prompt-attention competition, which is resolved by decomposition
into focused prompts.
EXPLANATION
The diagnostic detail is that adding logic-bug examples raised logic recall from 34 to 41
percent while dropping API-design recall from 82 to 68 percent. That inverse relationship
shows the two concerns are competing for the same finite attention and instruction budget
inside one prompt, so every improvement to one is paid for by the other. Decomposition
removes the competition entirely: run one review call scoped to security and API design with
its own examples and criteria, run another scoped to business-logic correctness with its own
edge-case examples, and merge and deduplicate the findings before posting a single
consolidated comment on the pull request. Each prompt can now be tuned, evaluated, and
regressed independently, which is a substantial operational improvement because you can see
immediately which reviewer changed behaviour. The cost is more API calls, but the shared
context, namely the diff and any repository conventions, can be placed in a cached prefix so
the marginal expense is modest, and the two calls can run in parallel so wall-clock latency
barely changes. In CI this pattern also lets you assign different severities, different blocking
policies, or even different model tiers per concern. A common misconception is that a stronger
model dissolves the trade-off; capability raises the ceiling for both categories but does not
eliminate the attention competition created by asking a single generation to optimise several
unrelated objectives at once, so decomposition remains the correct architecture regardless of
tier. KEY TAKEAWAYS
? Inverse recall movement between categories signals attention competition within a single
overloaded prompt.
? Decompose into focused prompts with dedicated criteria and examples, then merge and
deduplicate findings.
? Independent prompts can be evaluated, tuned, and regression-tested per concern.
? Use prompt caching for shared context and run the calls in parallel to control cost and
latency.
Question #7
After deploying automated code review, developers report that approximately 35% of flaggedfindings are false positives falling into consistent patterns: style suggestions contradictingteam conventions, security warnings for patterns that are safe in your deployment context,and performance suggestions that would degrade your specific use case. You want to reducefalse positives while maintaining the ability to catch genuine issues. Which approach bestenables the model to generalize its judgment to novel code patterns it has not seen before?
A. Implement post-processing that uses keyword matching to filter out findings containingterms such as “convention,” “context-dependent,” or “trade-off.”
B. Include few-shot examples in your prompt showing annotated code snippets thatdistinguish acceptable patterns from genuine issues in each category.
C. Create a comprehensive written specification of all patterns that should not be flagged,and then include the full documentation in the system prompt.
D. Addinstructions to your system prompt to “be conservative,” “only flag definite issues,”and “consider that some patterns may be intentional.”
Answer: B EXPERT VERIFICATION The original answer was correct.
The original answer was correct: the question specifically asks for generalisation to unseen
code patterns, which is the defining strength of few-shot demonstrations over exhaustive rule
lists.
EXPLANATION
Annotated few-shot examples teach a decision boundary rather than a lookup table. By
showing paired snippets in each problem category, one representing a pattern that is
acceptable in your deployment context and one representing a genuine defect, together with
the reasoning that separates them, you give the model the underlying principle it can extend
to code it has never seen. That is exactly what is required here, because the false positives
cluster into consistent categories, style versus team convention, security warnings that are
safe in this deployment context, and performance advice that is wrong for this workload, but
the specific code will differ on every pull request. Demonstration-based prompting is generally
the most token-efficient way to transfer nuanced judgement, since a handful of well-chosen
contrastive pairs conveys more than pages of prose. Best practice is to select examples from
your own repository so they carry authentic idioms, cover each false-positive category with at
least one contrastive pair, keep the annotation short and focused on the discriminating reason,
place the examples in the system prompt so they are stable, and mark that block for prompt
caching so the examples cost almost nothing on repeat reviews. Maintain the set from real
dismissal data and re-run your evaluation suite whenever it changes. A common
misconception is that an exhaustive written specification of everything not to flag is more
rigorous; in practice such documents are never complete, they grow unmaintainably, and they
fail on the first novel pattern precisely because they enumerate instances instead of teaching
the distinction. KEY TAKEAWAYS
? Few-shot contrastive examples teach a decision boundary that generalises to unseen code.
? Use real snippets from your own repository, one acceptable and one genuine issue per
category.
? Exhaustive rule lists cannot be complete and fail on novel patterns they never enumerated.
? Put the examples in a cached system prompt block and refresh them from real dismissal
data
Question #8
After investigating a billing dispute for more than 25 turns, you determine that duplicate
charges resulted from a payment-gateway timeout triggering retry logic. The required refund
of $847 exceeds your $500 authorization limit, so you must invoke escalate_to_human. The
human agent will not have access to the conversation transcript. What context should you
pass to enable effective resolution?
A. The complete conversation transcript containing every message and tool result.
B. The customer’s original complaint verbatim together with excerpts from the tool results
showing the duplicate transactions.
C. A structured summary containing the customer identifier, verified root cause, refund
amount, relevant transaction identifiers, actions already attempted, and recommended
next action.
D. Only the diagnosis and refund amount.
Answer:C E X P E R T V E R I F I C A T I O N ? The original answer was correct.
The original answer was correct; a human receiving a handoff without the transcript needs a
structured, decision-ready summary rather than a raw log or a bare conclusion. E X P L A N A T I O N Escalation is a context-handoff problem with a human on the receiving end, and the design
rule is the same as for agent-to-agent handoffs: pass exactly what the recipient needs to act,
in a structure they can scan. Twenty-five turns of transcript is the raw material, not the
deliverable; dumping it forces the human to redo the investigation the agent has already
completed and buries the decision under tool calls and dead ends. A bare diagnosis and
amount goes too far the other way, giving a number the human cannot verify or defend when
the refund exceeds the agent's authorisation limit. The structured summary sits at the right
level: the customer identifier so the record can be pulled, the verified root cause so the human
understands why the charges duplicated, the refund amount so the authorisation decision is
explicit, the transaction identifiers so every claim can be checked against the payment system,
the actions already attempted so nothing is repeated and no partial state is missed, and a
recommended next action so the human is approving a proposal rather than starting from
zero. This is the standard escalation contract in production customer-service agents, and it is
best implemented as a strict tool input schema on escalate_to_human so the fields cannot be
omitted, with the summary generated from the agent's own verified findings and the full
transcript retained separately for audit and quality review. Common misconceptions are that more context is always safer and that transcripts are self-documenting; both increase human
handling time and reduce the chance the salient facts are seen. K E Y T A K E A W A Y S ? Escalation handoffs should be structured, verifiable, and decision-ready, not raw transcripts.
? Include identity, root cause, amount, transaction identifiers, actions attempted, and a
recommended next action.
? Enforce the contract with a strict tool input schema so required fields cannot be omitted.
? Keep the full transcript in logs for audit while passing the summary to the human.
Question #9
Your automated review calls the Claude API for each pull request, using tool_use with a
report_findings tool that returns a JSON array of finding objects. Each object contains file_path,
line_number, severity, category, and description. During testing on a large pull request
touching more than 30 files, the response reaches the max_tokens limit and is truncated in the
middle of the JSON, causing your pipeline’s parser to fail. What is the most effective way to
handle this?
A. Split the review into multiple API calls that each analyze a subset of the changed files, and
then merge the resulting findings arrays.
B. Increase max_tokens to the model’s maximum and instruct Claude to keep each finding
description under 50 words.
C. Switch from tool_use to prompting Claude to return findings as a Markdown list.
D. Add retry logic that detects truncated JSON and resends the request with instructions to
report only critical- and high-severity findings.
Answer:A E X P E R T V E R I F I C A T I O N ? The original answer was correct.
The original answer was correct; partitioning the diff across several requests is the only option
that bounds output size structurally rather than hoping a larger max_tokens is enough. E X P L A N A T I O N Hitting max_tokens mid-JSON is a structural problem, not a tuning problem. The volume of
findings scales with the size of the pull request, so any fixed output ceiling will eventually be
exceeded by a large enough change set. Splitting the review so each API call analyses a
bounded subset of the changed files, then merging the returned findings arrays in your
pipeline, makes the worst-case output per call predictable and keeps every response a
complete, parseable tool_use block. The approach has useful side effects: the per-file calls are
independent, so they can run concurrently to cut wall-clock time, or be submitted through the
Message Batches API for a substantial cost reduction on non-blocking reviews; each call has a
smaller context so the model attends more carefully to each file; and a failure affects one
shard rather than the whole review. Implementation notes: chunk by file or by diff-hunk token
budget rather than by a fixed file count, cache the shared prefix such as coding standards and
repository context so the repeated portion is cheap, always inspect stop_reason and treat
max_tokens as a hard error rather than parsing optimistically, and de-duplicate findings during
the merge because a shared helper may be flagged from two shards. The misconception worth
naming is that raising max_tokens to the model maximum is a fix; it raises the ceiling but leaves the pipeline one unusually large pull request away from the same silent truncation, and
it makes the failure rarer and therefore harder to catch in testing. K E Y T A K E A W A Y S ? Truncated tool_use output is a scaling problem; bound it by partitioning input, not by raising
max_tokens.
? Shard by token budget across files, then merge and de-duplicate findings in the pipeline.
? Always check stop_reason and treat max_tokens as a hard failure rather than parsing a
partial response.
? Independent shards enable concurrency or batch submission and improve per-file attention.
Question #10
You are integrating Claude Code into your Continuous Integration/Continuous Deployment
(CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides
feedback on pull requests. You need to design prompts that provide actionable feedback and
minimize false positives.
Your automated review calls the Claude API for each pull request, using tool_use with a
report_findings tool that returns a JSON array of finding objects. Each object contains file_path,
line_number, severity, category, and description. During testing on a large pull request
touching more than 30 files, the response reaches the max_tokens limit and is truncated in the
middle of the JSON, causing your pipeline’s parser to fail.
What is the most effective way to handle this?
A. Split the review into multiple API calls that each analyze a subset of the changed files, and
then merge the resulting findings arrays.
B. Increase max_tokens to the model’s maximum and instruct Claude to keep each finding
description under 50 words.
C. Switch from tool_use to prompting Claude to return findings as a Markdown list.
D. Add retry logic that detects truncated JSON and resends the request with instructions to
report only critical and high-severity findings.
Answer:A E X P E R T V E R I F I C A T I O N ? The original answer was correct.
The original answer was correct: the failure is that required output exceeds the output budget
on large pull requests, and partitioning the work across calls is the only fix that scales with pull
request size. E X P L A N A T I O N Every Claude model has a maximum output token limit per response, and when generation
hits it the response stops mid-token with stop_reason set to max_tokens. Because tool_use
input is streamed as JSON, truncation leaves structurally invalid JSON and any strict parser
fails. The underlying problem is a mismatch between the volume of findings a thirty-plus file
pull request produces and what one response can emit, so the durable remedy is to reduce the
output required per call. Partitioning the review by changed file, or by batches of files sized to
a token budget, gives each call a bounded amount to report, and the pipeline then
concatenates the resulting findings arrays and deduplicates before posting. This scales
naturally: a pull request twice the size simply produces twice as many calls. It also brings
practical benefits, since the calls are independent and can run in parallel to cut wall-clock latency, a single failed partition can be retried without redoing the whole review, and the
stable system prompt and review instructions can be prompt-cached across partitions so the
repeated prefix is cheap. If the review is not latency sensitive, the same partitioned work is a
natural fit for the Message Batches API at reduced cost. Defensive engineering still applies:
always check stop_reason rather than assuming a well-formed response, and fail loudly on
truncation. Raising max_tokens and shortening descriptions only postpones the ceiling, and
abandoning tool use for Markdown trades a structured contract for a fragile parse without
solving the size problem. K E Y T A K E A W A Y S ? Truncation at max_tokens yields invalid JSON in tool_use; always inspect stop_reason.
? Partition large workloads into bounded calls and merge plus deduplicate the results.
? Partitioned calls parallelise, retry independently, and benefit from prompt caching of the
shared prefix.
? Raising max_tokens is a ceiling shift, not a scaling strategy.
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