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КыргызчаConversational BI: SQL/Semantic with Quality Controls
LLM prompts for SQL/semantic queries with schema hints, quality checks, RBAC and observability are being developed.
Conversational BI must combine LLM query generation with strict schema verification, access rights, correctness tests, and auditing to prevent incorrect or dangerous responses.
Prompt Catalog, Metrics, Policies
SQL/semantic DSL; constrained decoding; templates.
Auto-suggest joins/filters with lineage.
Results with Caveats; Charts; Saved Questions
Feedback loops; reranking of candidates.
Observability: logs of prompts/queries/results/errors.
Frequently asked questions
How can a sandbox environment be initiated for execution, logging and feedback collection?
A sandbox environment can be initiated for execution, logging and collecting feedback to isolate and test the system effectively.
What metrics should be monitored regarding quality, errors, and latency?
Quality, error rates, and query latency should be continuously monitored to identify areas for improvement in templates or rules.
If incorrect answers are detected, what methods should be used for testing, selection criteria, and feedback weighting?
Incorrect answers can be addressed through the creation of test cases, careful selection of candidate responses, and incorporating feedback weighting to refine the system's accuracy.
How is access to Personally Identifiable Information (PII) managed – including masking, policies, and auditing?
Access to PII is governed by robust masking techniques, strict data governance policies, and comprehensive audit trails for accountability.
▶ Try it live
Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.