When analyzing an agent's failure to complete multi-step financial analysis tasks, which evaluation approach best identifies prompt engineering…

For complex, multi-step tasks, failure often occurs in the intermediate reasoning or planning stages. A systematic approach is required to diagnose this. Testing with chain-of-thought (CoT) templates explicitly evaluates the model's reasoning process. Analyzing the step-by-step decomposition reveals where the agent's plan breaks down. Tracking success rates across different complexities helps identify the boundaries of the agent's capabilities. This structured, analytical approach directly targets the prompt's influence on task decomposition and execution, providing actionable insights for improvement, which is far superior to only checking the final answer or using generic prompts.

B. Optimizing for speed over reasoning quality is counterproductive for financial analysis, where accuracy and correctness are the primary requirements.

C. Testing only the final output is a black-box method that hides which intermediate step failed, making it impossible to debug the decomposition logic in the prompt.

D. Generic templates are unlikely to work for a specialized domain like finance, which requires specific terminology, formats, and reasoning patterns for reliable performance.

1. Wei, J., et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv preprint arXiv:2201.11903. This foundational paper demonstrates that prompting LLMs to generate intermediate reasoning steps (a chain of thought) improves performance on complex tasks, validating the focus on step-by-step decomposition analysis. (See Tables 3 and 4 for results).

2. Yao, S., et al. (2023). Tree of Thoughts: Deliberate Problem Solving with Large Language Models. arXiv preprint arXiv:2305.10601. This work extends CoT by exploring multiple reasoning paths, reinforcing the principle that evaluating and improving the intermediate thought process is key to solving complex tasks. (Section 2).

3. NVIDIA Developer Blog. (2023). A Practical Guide to Prompt Engineering with Llama 2. Discusses techniques like few-shot prompting and providing structured examples, which are core components of systematic prompt testing to improve task execution for specialized domains.