How do you prevent an LLM from drifting off-topic in a multi-step retrieval scenario (ensuring each step’s query remains relevant to the original question), and how would that be evaluated?

To prevent an LLM from drifting off-topic in multi-step retrieval, you need to enforce strict context management and iterative validation. One approach is to design the process so that each step’s output is explicitly tied to the original query. For example, you can structure prompts to include the original question at every step, along with a directive to focus only on that context. This forces the model to re-anchor its reasoning to the user’s intent. Another method is to decompose the original query into smaller, self-contained sub-queries upfront, ensuring each step addresses a specific facet of the problem. For instance, if the user asks, “How do I optimize a Python script for memory usage?”, sub-queries might include “identify memory-intensive Python patterns” and “tools to profile memory in Python.” By predefining these steps, you reduce the risk of tangential exploration. Additionally, intermediate validation checks can be added—like comparing the relevance of a generated query to the original question using similarity metrics—to flag and correct deviations early.

Evaluation involves measuring relevance at each step and overall coherence. One metric is query-to-original similarity: compute the cosine similarity between embeddings of the original question and each step’s output to quantify alignment. For example, if a step generates a query about “database optimization” for a question focused on “Python memory,” a low similarity score would flag it as off-topic. Human evaluation is also critical. Annotators can rate each step’s relevance on a scale (e.g., 1–5) and track how often the system self-corrects after validation. Automated tests using predefined scenarios can simulate multi-step interactions—like testing whether a medical diagnosis assistant stays focused on symptoms mentioned in the initial query—and measure success rates. Tools like precision (percentage of relevant steps) and recall (coverage of required subtopics) provide quantitative benchmarks. For instance, if 8 out of 10 steps in a retrieval pipeline directly address the original question, precision is 80%.

A practical example is a technical support chatbot handling a multi-issue ticket like “My app crashes on startup, and the logs show a memory error.” The first step might retrieve “common causes of app crashes on startup,” the second could focus on “interpreting memory-related log errors,” and the third might gather “debugging tools for memory leaks.” Each step’s query must exclude unrelated topics (e.g., network issues). During evaluation, you’d track whether all retrieved information pertains to crashes and memory. If the second step retrieves “network latency diagnostics” instead of memory tools, the system would either adjust the query automatically (via validation) or flag it for review. This combination of structured prompting, validation layers, and relevance metrics ensures the LLM stays on track while providing measurable performance indicators for developers to refine the system.