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implementation
Implement DSPy for Prompt Optimization
Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.
Linked challenge: Orchestrate a GPT-5 & Cohere R+ Quality Assurance Crew with DSPy & MCP
Format
Text-first
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Linked challenge
Orchestrate a GPT-5 & Cohere R+ Quality Assurance Crew with DSPy & MCP
Prompt source
Original prompt text with formatting preserved for inspection.
1 lines
1 sections
No variables
0 checklist items
Implement a DSPy pipeline to programmatically optimize the prompts for your 'Content Analyzer' agent, specifically focusing on improving its ability to detect subtle 'hallucinations' or factual inaccuracies. Describe the chosen `Signature`, `Predict` module, and how you will use a small dataset of example content with known errors to `compile` the DSPy program. Provide relevant code snippets.
Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Hold the task contract and output shape stable so generated implementations remain comparable.
Tune next
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Verify after
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.