Who should use the Question answering workflow?
Teams or solo builders working on work tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Work
Practical execution plan for question answering with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
Continuous improvement of answer quality and system reliability
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
Continuous improvement of answer quality and system reliability
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Msty to a well-defined, unambiguous question with a clear scope of knowledge sources. Then, you pass the output to Weaviate to a ranked list of relevant text passages that likely contain the answer. Then, you pass the output to Anthropic Console to a concise answer with inline citations to the source documents. Then, you pass the output to Microsoft Copilot to a polished, verified answer ready for the user. Then, you pass the output to Microsoft Bot Framework to user receives a trustworthy answer with traceable sources and confidence level. Finally, Sisense is used to continuous improvement of answer quality and system reliability.
Define the Question & Context Scope
A well-defined, unambiguous question with a clear scope of knowledge sources
Retrieve Relevant Information
A ranked list of relevant text passages that likely contain the answer
Synthesize the Answer with Citations
A concise answer with inline citations to the source documents
Review and Refine Output
A polished, verified answer ready for the user
Deliver Answer with Source Transparency
User receives a trustworthy answer with traceable sources and confidence level
Log and Iterate (Optional)
Continuous improvement of answer quality and system reliability
Clarify the user's intent, domain, and any constraints (e.g., document set, time range, language). If the question is ambiguous, ask clarifying questions or rephrase it into a precise query. This prevents hallucination and ensures the answer is relevant.
Why Msty: Msty supports prompt engineering and chat with LLMs, which directly aligns with defining the question and context scope via LLM interaction.
Use a retrieval system (vector search, keyword search, or hybrid) to fetch the most relevant chunks or documents from the knowledge base. Rank results by relevance score and keep the top K (e.g., 5-10) to stay within context limits.
Why Weaviate: Weaviate is a vector database with semantic search and RAG capabilities, exactly matching the need for vector storage and retrieval.
Feed the retrieved passages and the original question into an LLM with a prompt that instructs it to answer concisely, cite specific passages, and indicate confidence. The model should not add external knowledge. If no passage supports an answer, respond 'I don't know.'
Why Anthropic Console: Anthropic Console provides prompt engineering and model evaluation, suitable for synthesizing answers with citations using an LLM.
Check the answer for factual accuracy, completeness, and readability. If the answer is too long, summarize; if too short, expand with additional relevant details from the retrieved passages. Ensure the tone matches the user's context (e.g., technical vs. layperson).
Why Microsoft Copilot: Microsoft Copilot can answer questions, generate text, and summarize, which supports reviewing and refining output via LLM revision.
Present the final answer to the user along with the source documents or passages used. Optionally include a confidence score (e.g., 'High confidence' if multiple sources agree). This builds trust and allows the user to verify.
Why Microsoft Bot Framework: Microsoft Bot Framework enables omnichannel message routing and conversation management, ideal for delivering answers through various channels.
Record the question, retrieved passages, answer, and user feedback (if any) to improve future retrieval and generation. If the user corrects the answer, update the knowledge base or fine-tune the retrieval pipeline.
Why Sisense: Sisense provides data visualization and embedded analytics, fitting the need for a logging database and analytics dashboard.
§ Before you start
Teams or solo builders working on work tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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