An LLM can perform multi-step retrieval by iteratively refining its search process using intermediate results. The process starts with the LLM analyzing the initial query to generate a first retrieval request—for example, querying a database or searching a document store. The retrieved data is then evaluated by the LLM to determine if it fully answers the question or if gaps remain. If additional context is needed, the LLM formulates a new, more precise query based on the initial results, incorporating keywords, context, or clarifications. This loop continues until the LLM gathers sufficient information to produce a final answer.
For example, consider a user asking, "What caused the decline of Company X, and how did it impact the tech industry?" The LLM might first retrieve a company timeline, identify a specific event (e.g., a product failure), then search for industry reports mentioning that event. If the reports lack details, the LLM could pivot to querying news articles or financial filings to fill in gaps, such as stock price drops or competitor reactions. Each step uses the prior result to narrow the focus, ensuring the final answer synthesizes multiple sources. Tools like vector databases or APIs (e.g., web search) enable this chaining by allowing the LLM to programmatically fetch and filter data at each stage.
Key considerations include balancing depth with efficiency. Multi-step retrieval increases latency, so developers often limit iterations (e.g., 3–5 steps) or use heuristics to decide when to stop. Error handling is critical: if the LLM misinterprets early results, subsequent queries may compound mistakes. Techniques like validating retrieved data against known sources or prompting the LLM to self-critique its intermediate steps can mitigate this. Frameworks like LangChain or LlamaIndex simplify implementation by providing built-in workflows for chaining retrieval and generation steps, while allowing customization of query logic and data sources.