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Refine Reasoning for Hallucination Reduction
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Linked challenge: Build a Model-First Reasoning Agent for Multi-Tier Supply Chain Optimization using LangGraph & Llama 3.3
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Linked challenge
Build a Model-First Reasoning Agent for Multi-Tier Supply Chain Optimization using LangGraph & Llama 3.3
Prompt source
Original prompt text with formatting preserved for inspection.
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1 sections
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Analyze the `reasoning_trace` generated by Llama 3.3 during initial simulation runs. Identify instances where the agent's reasoning seems to deviate from the explicit supply chain model or produces illogical actions (hallucinations). Refine your LangGraph state transitions, tool definitions, and Llama 3.3 prompts (e.g., by adding more constraints, few-shot examples, or explicit validation steps) to mitigate these issues and improve reasoning coherence and accuracy.
Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Preserve the rubric, target behavior, and pass-fail criteria as the baseline for evaluation.
Tune next
Adjust fixtures, mocks, and thresholds to the system under test instead of weakening the assertions.
Verify after
Make sure the prompt catches regressions instead of just mirroring the happy-path examples.