LLMs generate accurate multi-step analytic plans, and micro data lake, for each question
Analytic plans begin with a question
Each question establishes a context for an analytic plan. This includes which databases, tables, and columns are relevant. It also includes steps needed to answer the question. Analytic plans are organized sequentially, beginning with a join graph of columns and tables. Other steps include statistical measures, logical features, groupings and aggregations.
Binding the LLM for reliable plans
The LLM is bound to a step-wise prompt and response pattern. It focuses first on identifying the data needed. Then it moves to sequential steps including data cleansing and normalization. These steps also include statistical measures, logical features, grouping, and aggregations. The LLM is not given freedom to deviate from the sequenced, ordered steps.
Human and automated plan validation
As the LLM generates steps in the plan, the steps are validated automatically. Errors are corrected as they occur. Each completed plan is saved for human review and validation. It can also be modified. This ensures that LLM use is transparent, easily explained, and auditable.
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Automated database context
LangGrant automatically delivers complete database context for LLMs to comprehend multiple databases simultaneously at scale. Like a skilled engineer, once an LLM understands databases it can contribute to solution design.
Micro data lakes on demand
LangGrant binds LLMs to create accurate analytic plans for user queries, resulting in a inference ready “micro data lake.” Plans are saved, easily validated and modified, and run to deliver the analytic data within minutes of the user query.
Governance
PII safeguards, authorization controls, data residency rules, firewall restrictions, and token-governance policies are built-in by design. No sensitive data leaves governed systems.
Plan management
LLM generated plans are saved, easily reviewed and validated, modified, and executed, for LLM use that is transparent, explainable, and repeatable.
Database cloning and containers
On demand database clones with containers provide Agent developers with production database copies (with optional masking) for agentic AI dev/test.
Database subsetting and synthetic data
Database subsetting with synthetic data provides added context for working with complex multi-database environments.