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Pain points in business: what people struggle with
Search the problems to solve, as people described them on Reddit and other platforms, in their own words.
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Stale RAG Knowledge Retrieval
AI and RAG systems struggle to retrieve accurate, current, and relationship-aware knowledge because documents are scattered, versioned inconsistently, and go stale without maintenance.
In their words
someone could make a fortune if they invented a program that deletes repeat emails in doc review so that we don’t have to review the same thread 27 different times
I need provenance: which passage is the first occurrence vs which is a later description of it.
Only drawback is the generation which is llm based itself for the documents part which has a cost and bias factor attached to it.
What I need is an unattended verification step in a pipeline running over thousands of articles.
users just mash the search bar with 1 or 2 vague words, forcing the serverless functions to do massive fuzzy lookups
most people just search one or two words. so feels like helping ppl know what to search for is the actual thing that’s needed
In real technical environments, the answer to a single question is scattered across multiple formats.
I regularly work on corpuses in the hundreds of billions of documents. There is no plausible future where a context window holds an large org's entire corpus.
How are you checking that trace clusters reflect real intent instead of surface wording and who owns the taxonomy once it starts changing?
llm-wiki must have a git mechanism to roll back safely, which is missing from the day-0 design
one retrieval strategy for every use case was the wrong call early on and cost real rework to unwind
I really need it to get deep into detail on what goes on each slide, and to do that, it needs to analyze content from multiple lengthy documents at once.
I think I'd rather, perhaps naively, whitelist wikipedia, reddit, arxiv, some news sources, etc than include everything. Is there nothing out there that does this?
This got expensive fast, so I'm wondering what your approach is to keep costs down across what I imagine is a lot of volume
RAG and AI knowledge teams
Where they talk
- r/Rag
- r/LLMDevs
- r/AI_Agents
- r/LocalLLaMA
- r/LangChain
- r/ClaudeAI
- r/ClaudeCode
- r/ContextEngineering
Tools they use today
- Claude
- Neo4j
- pgvector
- RRF
- Claude Code
- SQLite
- ChatGPT
- Obsidian
Why those fall short
- top-5 similar chunks may not retrieve the full chain
- feeds repeat same kind of content; no tool does the whole thing
- no incremental indexing; unchanged files are re-embedded every run
- fuzzy lookups return whole catalog; dedicated search engines shift cost
- Swapping backends doesn't fix vague user inputs; users still get unfiltered results
Map of ideas
Drawing the map…