How can one improve the relevance or quality of DeepResearch's output if the initial results are not satisfactory?

To improve the relevance or quality of DeepResearch’s output when initial results are unsatisfactory, start by refining the input query and adjusting search parameters. DeepResearch relies heavily on the specificity and clarity of the input to generate accurate results. For example, if a query like “AI trends” returns broad or irrelevant results, rephrasing it to “recent advancements in transformer-based models for NLP applications” provides clearer context. Adding filters such as date ranges (e.g., “since 2022”), domain-specific keywords (e.g., “healthcare” or “finance”), or excluding unrelated terms (e.g., "-marketing") can further narrow the scope. Developers should test variations of the query iteratively to identify which terms or constraints yield the most relevant data.

Next, adjust the tool’s configuration settings to align with the desired output. Many research tools allow users to prioritize sources (e.g., peer-reviewed journals vs. blogs), set confidence thresholds for results, or control the depth of analysis. For instance, if DeepResearch is returning too many low-quality sources, increasing the minimum credibility score for included documents can filter out unreliable content. Similarly, if the output lacks technical depth, enabling an “expert mode” (if available) might prioritize highly specialized content. Developers should also verify whether the tool supports custom ranking algorithms—for example, weighting recent publications more heavily than older ones in fast-moving fields like AI or cybersecurity. These tweaks require experimentation but can significantly improve relevance.

Finally, incorporate feedback loops and post-processing steps. If DeepResearch allows user feedback (e.g., marking results as “relevant” or “irrelevant”), use this feature to train the system over time. For example, if the tool repeatedly surfaces outdated research papers, flagging them as irrelevant helps it learn to prioritize newer data. Additionally, manually reviewing and categorizing initial outputs can uncover patterns—such as common keywords or recurring low-quality sources—that inform further query refinements. Post-processing tools like scripts to deduplicate results, cluster similar findings, or extract key insights (e.g., using regex or NLP libraries) can also enhance the final output. For instance, a Python script could filter results to include only papers with specific methodologies (e.g., “randomized controlled trials”) mentioned in their abstracts. Combining these strategies ensures continuous improvement in result quality.