A single point of failure is an Architect's nightmare. This task focuses on **Reciprocal Verification**—the practice of having Claude review its own work (Self-Correction) or having a second instance act as a "Judge." You will learn to build closed-loop pipelines that iteratively refine outputs until they meet a predefined quality threshold.
In scientific journals, a paper isn't published just because the author thinks it's correct. It goes through **Peer Review**. Other experts look for flaws, question the logic, and demand changes. The author then fixes those issues, and the paper is reviewed again. Only after this loop is it "Verified."
Feedback Loops transform Claude from an "Agent" into a "Peer-Reviewed System." You are building the mechanisms that catch errors before they ever leave the pipe.
Rather than asking for the final answer once, ask Claude to 1. Generate, 2. Review for errors, and 3. Finalize.
Turn 1 (Prompt): "Identify bugs in [File]." Turn 2 (Follow-up): "Now, check your list of bugs. Are any of these false positives? If so, remove them and return a final validated JSON."
The "Self-Check" turn consistently reduces hallucinations by ~25% because it provides Claude a second chance to verify its reasoning within the same context.
To maximize accuracy, use two independent Claude calls (potentially with different roles or different models like Sonnet and Opus).
In high-throughput systems, you can't review everything. Use **Dynamic Verification Thresholds**.
1. The "Confidence Score": Ask Claude to return a score (0-1) in its JSON.
2. Threshold: If score > 0.95, accept. If < 0.95, trigger the Reviewer Loop.
3. Dead-end: If after 3 loops the score is still < 0.8, escalate to a human reviewer.
This "Escalation Matrix" ensures you only pay the token cost of feedback loops for the difficult ambiguous cases, rather than for the easy "Happy Path" requests.
Design your system to track Max Verification Budget. A loop that never ends is a token drain. Limit retries to 3 passes, after which the system must return a "Confidence Failure" status code to the user or human operator.
Using the same prompt for both Generate and Critique. Problem: Claude Instance B will likely agree with Claude Instance A. Fix: Give the Critic an "Adversarial" prompt: "Try to find reasons why the Agent is wrong."
System gets stuck in a "Self-Correct" loop without a max count. Fix: Hard-code a maximum of 2 validation passes.
Sending a vague "This is wrong, fix it" to the Agent. Problem: Input noise increases. Fix: Feedback must be structural: "Tag [X] is missing, Key [Y] is not a string."
Running a 5-step loop for every simple query. Fix: Only trigger the loop for high-risk domains.
Scenario: You are building an IDE agent. Sometimes it produces Python code that has syntax errors. You want to ensure the code works before the user sees it.
Question: What's the most reliable feedback loop?
Correct Answer: B. Grounding a feedback loop in *external reality* (like a compiler/runtime error) is the most powerful accuracy booster in architectural design.