If DeepResearch provides an answer that conflicts with information you already have, start by verifying both sources. First, check the timestamps and context of the conflicting information. For example, if your internal documentation states that an API endpoint requires a v1 parameter, but DeepResearch suggests using v2, confirm whether the API version has been updated recently. Check the official documentation or release notes for that service to identify deprecations or changes. Similarly, validate the scope of the problem—DeepResearch might be referencing a different programming language, framework version, or environment configuration than the one you’re using.
Next, cross-reference the conflicting details with trusted resources. If DeepResearch claims a specific algorithm is more efficient but your tests show otherwise, replicate the experiment using the same dataset and parameters. For instance, if you’re comparing sorting algorithms, ensure both implementations are optimized and tested under identical conditions. If discrepancies persist, review community discussions (e.g., Stack Overflow, GitHub issues) or academic papers to see if others have encountered similar issues. This step helps uncover edge cases, bugs, or misunderstandings in either source. For example, a performance claim might assume hardware optimizations your setup lacks.
Finally, resolve the conflict by updating your knowledge base or code with clear documentation. If DeepResearch’s answer is correct, adjust your internal resources and note the reason for the change. If your original information is still valid, document why DeepResearch’s suggestion doesn’t apply—for example, a library version mismatch. Share your findings with your team through a brief write-up or code comments. For instance, you might add a note like, “DeepResearch suggests X, but our tests with Framework Y v3.2 show Z due to deprecated methods.” This ensures transparency and helps others avoid the same confusion in the future.