Processing high volumes of unstructured data (text)… we’re using a STAG architecture.
- Generate targeted LLM micro summaries of every record (ticket, call, etc.) continually
- Use layers of regex, semantic embeddings, and scoring enrichments to identify report rows (pivots on aggregates) worth attention, running on a schedule
- Proactively explain each report row by identifying what’s unusual about it and LLM summarizing a subset of the microsummaries.
- Push the result to webhook
Lack of JSON schema restriction is a significant barrier to entry on hooking LLMs up to a multi step process.
Another is preventing LLMs from adding intro or conclusion text.
- Generate targeted LLM micro summaries of every record (ticket, call, etc.) continually
- Use layers of regex, semantic embeddings, and scoring enrichments to identify report rows (pivots on aggregates) worth attention, running on a schedule
- Proactively explain each report row by identifying what’s unusual about it and LLM summarizing a subset of the microsummaries.
- Push the result to webhook
Lack of JSON schema restriction is a significant barrier to entry on hooking LLMs up to a multi step process.
Another is preventing LLMs from adding intro or conclusion text.