LLM Integration and Prompt Engineering for Agents
Integrate large language models into agents and use prompt engineering to ensure reliable agent behavior.
Many modern agents are powered by large language models (LLMs), which provide reasoning and natural language understanding. Effective LLM integration requires: (1) Prompt engineering that clearly describes the agent's role, constraints, and expected behavior, (2) Few-shot examples showing how to handle typical scenarios, (3) Output parsing to extract structured data from unstructured LLM responses, (4) Error handling for cases where the LLM refuses the task or produces invalid output, and (5) Cost/latency optimization (larger models are more capable but slower and more expensive). Prompts should be explicit about safety constraints, decision criteria, and how to handle ambiguity. Chain-of-thought prompting (asking…
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