To improve the relevance or quality of DeepResearch's output, start by refining the input parameters and queries. Ensure the prompts are specific, well-structured, and include sufficient context. For example, instead of a vague query like "analyze market trends," specify the industry, timeframe, and data sources: "Compare quarterly revenue growth (2022-2023) for SaaS companies in the cybersecurity sector using SEC filings and earnings reports." Providing examples of desired output formats (e.g., tables, bullet points) or including keywords to prioritize can also guide the model toward more relevant results. If the tool supports domain-specific tuning, adjust settings to align with the subject matter—for instance, emphasizing technical jargon for engineering topics or financial metrics for business analyses.
Next, experiment with adjusting the model’s configuration or leveraging iterative feedback. Many AI tools allow users to control parameters like temperature (randomness) or top-k sampling (diversity of responses). Lowering the temperature reduces variability, making outputs more focused, while increasing it encourages creativity, which might help in exploratory research. If DeepResearch supports fine-tuning, retrain the model on a curated dataset relevant to your domain to improve accuracy. For example, training on medical research papers will yield better results for healthcare-related queries. Additionally, use an iterative approach: run initial queries, identify gaps (e.g., missing sources or incomplete comparisons), and refine the prompt with explicit instructions to address those gaps, such as "Include peer-reviewed studies published after 2020" or "Exclude opinion pieces."
Finally, validate and post-process the outputs. Cross-check results against trusted sources or use automated scripts to filter out low-confidence data. For instance, if DeepResearch extracts statistics, verify them against official databases like government reports or industry benchmarks. Tools like regular expressions or custom parsers can help extract structured data (e.g., dates, metrics) from unstructured text. If the output lacks coherence, manually reorganize the content or use summarization algorithms to highlight key points. For collaborative projects, implement a peer-review step where domain experts flag inaccuracies or suggest additional angles. Combining automated validation with human oversight ensures both relevance and reliability, especially for critical applications like legal or academic research.