Prompt Engineering

Chain-of-thought prompting with explicit sequential decomposition is the most effective technique for complex multi-step analytical tasks. By instructing the model to break down the contract review into discrete steps (identify parties → extract dates → review terms → validate compliance), you create a structured reasoning path that prevents the agent from missing critical elements. This technique is specifically designed for tasks requiring intermediate reasoning steps and dramatically improves accuracy on complex analytical workflows.

Complex conditional logic and data validation (like inventory status checking) should be handled programmatically through agent actions, not relied upon within prompts. LLMs can be inconsistent with strict constraint adherence, especially under edge cases or when balancing multiple objectives. Moving inventory validation to an Apex action ensures deterministic filtering that executes before recommendations are generated, eliminating the possibility of out-of-stock suggestions. This follows the best practice of using prompts for reasoning and language generation, while delegating precise data operations to code.

The issue stems from how grounding data is retrieved and referenced, not just prompt wording. Configuring the retrieval action (Data Cloud query or API call) to explicitly sort by timestamp descending ensures the freshest data appears first in the context. Then, instructing the prompt to specifically reference 'the first record' or 'most recent data' creates a clear, deterministic link between retrieval order and prompt usage. This combines proper data engineering (sorted retrieval) with clear prompt instructions, addressing the root cause.

Dynamic prompt templates with variable injection offer the optimal balance of flexibility, maintainability, and performance. By classifying the inquiry type once (using intent detection or simple rules), you can inject specific tone instructions into a single prompt template at runtime. This approach keeps all logic in one agent configuration, makes tone guidelines easy to update centrally, and scales efficiently as you add more tone variations. It's the standard pattern for context-dependent prompt customization in production agents.

RAG with semantic search optimized for clinical relevance provides the most sophisticated solution. Unlike simple time-based filtering, semantic search can identify which historical information is contextually important (e.g., a resolved condition 3 years ago that's relevant to a new diagnosis) while deprioritizing truly irrelevant old data. RAG retrieves only the most relevant context chunks for the query, naturally filtering out noise. This approach balances recency with clinical relevance and scales better than hard-coded rules as medical complexity varies.

Prompt chaining with a dedicated classifier agent is the most reliable architecture for critical binary decisions like escalation. By separating the escalation decision into a focused classifier prompt optimized solely for that task (with clear yes/no output format), you achieve higher accuracy than mixing escalation logic with response generation. The classifier can be rigorously tested and tuned independently. This pattern is a best practice for high-stakes routing decisions and prevents the main agent from under-escalating due to competing objectives (helpfulness vs. safety).