To prevent an LLM from drifting off-topic in multi-step retrieval scenarios, the key lies in enforcing explicit context retention and validation at each step. One approach is to structure the process so that the original query and prior context are systematically included in every subsequent interaction. For example, when decomposing a complex question into sub-queries, the LLM can be instructed to reference the original question explicitly (e.g., “Based on [original question], generate a sub-query about X”). This acts as a built-in reminder, reducing ambiguity. Additionally, using constrained decoding techniques—such as requiring generated sub-queries to include specific keywords from the original prompt—can anchor the model’s focus. Tools like semantic routers or rule-based filters can also validate intermediate outputs by checking alignment with the original intent using similarity metrics (e.g., cosine similarity between embeddings) or keyword matching.
Evaluation involves both automated metrics and human judgment. Automated methods might track relevance scores (e.g., similarity between sub-query embeddings and the original question) or use a smaller classifier model trained to detect off-topic deviations. For example, if a sub-query’s similarity score drops below a predefined threshold, the system could flag it for correction. Human evaluation remains critical for nuanced cases, where annotators assess whether each step logically contributes to resolving the original question. End-to-end testing—measuring the accuracy of the final answer against ground-truth data—can also indirectly validate step relevance, though it doesn’t isolate individual step performance. Combining these approaches provides a balanced assessment of both per-step relevance and overall coherence.
A practical implementation might involve a pipeline where the LLM first decomposes the query into sub-questions, each validated by a classifier. If a sub-query is flagged, the system could trigger a refinement step (e.g., “Revise this sub-query to better align with [original question]”). Tools like LangChain or custom middleware can manage this flow, enforcing context propagation and validation rules. For example, in a medical diagnosis scenario, a sub-query about “treatment options” must stay tied to the original symptom described, avoiding generic tangents. By embedding checks at each step and using hybrid evaluation (automated metrics + human review), developers can iteratively improve the system’s ability to maintain focus while retaining flexibility for complex reasoning.