How can an LLM be used to perform multi-step retrieval? (For instance, the LLM uses the initial query to retrieve something, then formulates a new query based on what was found, etc.)

An LLM can perform multi-step retrieval by iteratively refining queries based on prior results, enabling deeper exploration of complex questions. This process involves using the LLM to generate follow-up queries, analyze retrieved information, and adjust the search strategy dynamically. For example, if a user asks, “What caused the 2008 financial crisis?” the LLM might first retrieve high-level summaries, identify gaps (e.g., unclear mentions of mortgage-backed securities), then craft a new query like “How did subprime mortgage defaults trigger bank collapses?” This cycle continues until the answer is sufficiently detailed or the available data is exhausted.

To implement this, developers can structure the LLM to act as a decision-maker that evaluates intermediate results. The first step is to process the initial query and generate a search prompt, such as extracting keywords or rephrasing the question for a database. After retrieving initial documents or data snippets, the LLM parses them to identify ambiguities or missing context. For instance, if a user asks for help debugging a Python error, the LLM might first retrieve general troubleshooting guides, notice the error relates to a specific library, then query documentation for that library’s exception handling. This requires integrating the LLM with retrieval tools (e.g., APIs, vector databases) and designing prompts that explicitly instruct the model to assess results and plan next steps.

Key challenges include preventing infinite loops and ensuring relevance. Developers can set iteration limits (e.g., 3-5 steps) or implement confidence thresholds where the LLM stops if it determines further searches won’t improve results. For example, a medical diagnosis assistant might stop after retrieving conflicting guidelines and escalate to a human. Tools like LangChain or LlamaIndex simplify building such pipelines by managing context between steps. Testing is critical: scenarios like partial data (e.g., missing API specs) require the LLM to handle uncertainty gracefully, perhaps by broadening the search scope instead of repeating failed queries. By combining structured prompting, retrieval integration, and error handling, multi-step retrieval becomes a powerful method for tackling open-ended questions.