Real ChatGPT answer · index seedZnvi09Ooh8UcompleteB2B software · AI and automation toolsinformational
What is the best architectural pattern for integrating a custom LLM backend with Slack so our campus coding club can query our internal documentation directly from a channel?
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The searches the AI actually ran for this prompt.
SEED QUERY
What is the best architectural pattern for integrating a custom LLM backend with Slack so our campus coding club can query our internal documentation directly from a channel?
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 2 queries…
Searches and sources
ChatGPT2 real searches · gpt-6
Slack Events API official documentation request signing acknowledgements Socket Mode Bolt framework Google ↗
retrieval augmented generation architecture chunking embeddings vector database citations documentation best practices Google ↗
- The Events API · docs.slack.dev
- Events · docs.slack.dev
- Design and Develop a RAG Solution on Azure - Azure Architecture Center · learn.microsoft.com
- Retrieval-Augmented Generation (RAG) with MongoDB - MongoDB Vector Search · mongodb.com
- Using Socket Mode · docs.slack.dev
- Creating an app with Bolt for Python · docs.slack.dev
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Slack Events API official documentation request signing acknowledgements Socket Mode Bolt framework OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
retrieval augmented generation architecture chunking embeddings vector database citations documentation best practices OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |