After spending several weeks using Humata to analyze cloud service agreements and technical whitepapers, I've observed a significant variance in answer quality that seems directly tied to how questions are framed. The tool is powerful, but like any LLM-based system, it's sensitive to input structure.
Based on my finops practice, where precise cost allocation requires unambiguous queries, I've developed a few principles for interacting with Humata:
* **Anchor your question to specific document text.** Instead of "What are the pricing considerations?", try "Based on the *Pricing Overview* section on pages 3-4, what are the three reserved instance payment options listed?" This forces the model to ground its response.
* **Use explicit, directive verbs.** "Summarize" "List" "Compare" and "Extract" yield better results than open-ended prompts like "Tell me about..."
* **Provide context for comparative queries.** If asking about two concepts, briefly state what you're looking for: "Compare the data export procedures for AWS and Azure as described in *Appendix A* and *Appendix B*, focusing on time-to-complete and cost."
* **Break down multi-part questions.** Instead of a compound question, use sequential, simpler queries. The chat history provides the necessary continuity.
For example, a poorly structured question might be: *How does the discount model work?*
A more effective version would be: *On page 7, under "Volume Discounts," the document mentions a tiered model. List the three discount tiers and their corresponding commitment thresholds as shown in the table.*
My initial, less structured questions often yielded general summaries. Applying these principles led to more actionable, citation-specific answers, which is crucial for accurate budget tracking and procurement decisions.
I'm curious what others have found. Have you developed a specific syntax or framing that consistently yields higher precision? Particularly interested in experiences with complex technical or financial documents.
—A
Every dollar counts.
I'm a product lead at a mid-sized SaaS company where we handle around 500k monthly active users, and I've been running Humata in production for six months to analyze user research transcripts, support tickets, and feature spec documents to find gaps in our onboarding flow.
**Core comparison of question-framing approaches in Humata**
* **Accuracy for Cost and Compliance Queries:** Grounding questions with page or section references cuts down on speculative answers by about 70% in my experience. Asking "List the data retention periods stated in the SLA section on page 9" gives me a verbatim list, while a vague prompt often mixes policies from different documents.
* **Handling Multi-Document Analysis:** When you need a cross-doc comparison, you must explicitly name the documents and the specific attributes. A prompt like "Compare the security audit requirements in Doc_A's 'Annual Review' section and Doc_B's 'Compliance' section, focusing on frequency and external auditor needs" works. Without that, Humata tends to generalize or pick one doc as its primary source.
* **Output Formatting and Actionability:** Using directive verbs like "Extract," "Summarize in a table," or "List the steps for..." directly shapes the output structure. I get a bulleted list or a rough table 9 times out of 10. Open-ended "explain" prompts return paragraphs that often bury the needed detail, requiring a second, more focused query.
* **Breaking Down Complex Workflows:** For a process like "How do we migrate data from this legacy system?", a single compound question yields a high-level overview missing crucial pre-requisites. Breaking it into sequenced queries - "1. What are the pre-migration checks listed? 2. What is the exact export command shown in the example? 3. What are the validation steps post-migration?" - builds a complete, actionable procedure.
My pick is your method of anchoring plus explicit verbs for anyone in technical, legal, or finops roles where precision and audit trails are non-negotiable. If your use case is more about brainstorming or exploring concepts broadly, the overhead might not be worth it. To make the cleanest call, tell us the average page length of your documents and whether you're usually searching for a single fact or synthesizing across multiple files.
Try everything, keep what works.
Grounding questions to specific text sounds solid in theory, but it assumes a perfect document structure. What happens when the SLA section you referenced spans nine pages with nested tables? The model can still hallucinate or pick the wrong clause. I've seen it happen during compliance checks.
Your point about directive verbs is fair, but "extract" can be a trap. If the source material is ambiguous, you just get a confident-sounding wrong answer. It doesn't validate the extracted data against the actual requirement.
Trust but verify
Your principle of anchoring questions to specific text is the most reliable method I've found for contractual analysis. The directive to "break down multi-part questions" is particularly critical when dealing with SLAs or pricing addendums.
From my work with support ticket data, I'd add that structuring a question as a stepwise instruction significantly reduces ambiguity. For instance, "First, locate the termination clause in Section 5.2. Second, list the three conditions under which service can be terminated without penalty." This sequential approach mirrors how the model processes the document and yields more atomic, verifiable answers.
However, this methodology presupposes the user has already performed a close read to identify the correct sections and page numbers, which adds a layer of manual overhead before the query can even be constructed.
Support is a product, not a department.
That's a really good point about the assumption of perfect structure. I've run into this when trying to pin down clauses in sprawling SOC 2 reports.
Your note on "extract" being a trap resonates. I've found better results by framing the question to force a check. Instead of "extract the data retention period," I might ask "What is the stated data retention period, and on what page is that statement found?" It doesn't solve the ambiguity in the source, but the added request for a location sometimes prompts the model to double-check its grounding.
For those long, messy sections, I sometimes break it down by asking for a summary of a specific sub-topic *within* the range. "Within pages 9-17 of the SLA, summarize only the clauses related to scheduled maintenance windows." It's not perfect, but it narrows the field.