TL;DR

Everyone builds a "Chat with your Data" bot eventually. But standard RAG fails when data is static (latency), exact (SQL table names), or noisy (Slack logs). Here are the three specific architectural patterns I used to solve those problems across three different products: Client-side Vector Search, Temporal Graphs, and Heuristic Signal Filtering.

The Story

I’ve been building AI-driven tools for a while now. I started in the no-code space, building “A.I. Agents” in n8n. Over the last several months I pivoted to coding solutions, many of which involve or revolve around RAG.

And like many, I hit the wall.

The "Hello World" of RAG is easy. But when you try to put it into production—where users want instant answers inside Excel, or need complex context about "when" something happened, or want to query a messy Slack history—the standard pattern breaks down.

I’ve built three distinct projects recently, each with unique constraints that forced me to abandon the "default" RAG architecture. Here is exactly how I architected them and the specific strategies I used to make them work.

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1. Formula AI (The "Mini" RAG)

The Build: An add-in for Google Sheets/Excel. The user opens a chat widget, describes what they want to do with their data, and the AI tells them which formula to use and where, writes it for them, and places the formula at the click of a button.

The Problem: Latency and Privacy. Sending every user query to a cloud vector database (like Pinecone or Weaviate) to search a static dictionary of Excel functions is overkill. It introduces network lag and unnecessary costs for a dataset that rarely changes.

The Strategy: Client-Side Vector Search I realized the "knowledge base" (the dictionary of Excel/Google functions) is finite. It’s not petabytes of data; it’s a few hundred rows.

Instead of a remote database, I turned the dataset into a portable vector search engine.

1. I took the entire function dictionary.
2. I generated vector embeddings and full-text indexes (tsvector) for every function description.
3. I exported this as a static JSON/binary object.
4. I host that file.

When the add-in loads, it fetches this "Mini-DB" once. Now, when the user types, the retrieval happens locally in the browser (or via a super-lightweight edge worker). The LLM receives the relevant formula context instantly without a heavy database query.

The 60-second mental model: [Static Data] -> [Pre-computed Embeddings] -> [JSON File] -> [Client Memory]

The Takeaway: You don’t always need a Vector Database. If your domain data is under 50MB and static (like documentation, syntax, or FAQs), compute your embeddings beforehand and ship them as a file. It’s faster, cheaper, and privacy-friendly.

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2. Context Mesh (The "Hybrid" Graph)

The Build: A hybrid retrieval system that combines vector search, full-text retrieval, SQL, and graph search into a single answer. It allows LLMs to query databases intelligently while understanding the relationships between data points.

The Problem: Vector search is terrible at exactness and time.

1. If you search for "Order table", vectors might give you "shipping logs" (semantically similar) rather than the actual SQL table tbl_orders_001.
2. If you search "Why did the server crash?", vectors give you the fact of the crash, but not the sequence of events leading up to it.

The Strategy: Trigrams + Temporal Graphs I approached this with a two-pronged solution:

Part A: Trigrams for Structure To solve the SQL schema problem, I use Trigram Similarity (specifically pg_trgm in Postgres). Vectors understand meaning, but Trigrams understand spelling. If the LLM needs a table name, we use Trigrams/ilike to find the exact match, and only use vectors to find the relevant SQL syntax.

Part B: The Temporal Graph Data isn’t just what happened, but when and in relation to what. In a standard vector store, "Server Crash" from 2020 looks the same as "Server Crash" from today. I implemented a lightweight graph where Time and Events are nodes.

[User] --(commented)--> [Ticket] --(happened_at)--> [Event Node: Tuesday 10am]

When retrieving, even if the vector match is imperfect, the graph provides "relevant adjacency." We can see that the crash coincided with "Deployment 001" because they share a temporal node in the graph.

The Takeaway: Context is relational. Don’t just chuck text into a vector store. Even a shallow graph (linking Users, Orders, and Time) provides the "connective tissue" that pure vector search misses.

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3. Slack Brain (The "Noise" Filter)

The Build: A connected knowledge hub inside Slack. It ingests files (PDFs, Videos, CSVs) and chat history, turning them into a queryable brain.

The Problem: Signal to Noise Ratio. Slack is 90% noise. "Good morning," "Lunch?", "lol." If you blindly feed all this into an LLM or vector store, you dilute your signal and bankrupt your API credits. Additionally, unstructured data (videos) and structured data (CSVs) need different treatment.

The Strategy: Heuristic Filtering & Normalization I realized we can’t rely on the AI to decide what is important—that’s too expensive. We need to filter before we embed.

Step A: The Heuristic Gate We identify "Important Threads" programmatically using a set of rigid rules—No AI involved yet.

• Is the thread inactive for X hours? (It’s finished).
• Does it have > 1 participant? (It’s a conversation, not a monologue).
• Does it follow a Q&A pattern? (e.g., ends with "Thanks" or "Fixed").
• Does it contain specific keywords indicating a solution?

Only if a thread passes these gates do we pass it to the LLM to summarize and embed.

Step B: Aggressive Normalization To make the LLM’s life easier, we reduce all file types to the lowest common denominator:

• Documents/Transcripts → .md files (ideal for dense retrieval).
• Structured Data → .csv rows (ideal for code interpreter/analysis).

The Takeaway: Don’t use AI to filter noise. Use code. Simple logical heuristics are free, fast, and surprisingly effective at curating high-quality training data from messy chat logs.

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Closing Thoughts

We are moving past the phase of "I sent a prompt to OpenAI and got an answer." The next generation of AI apps requires composite architectures.

• Formula AI taught me that sometimes the best database is a JSON file in memory.
• Context Mesh taught me that "time" and "spelling" are just as important as semantic meaning.
• Slack Brain taught me that heuristics save your wallet, and strict normalization saves your context.

Don’t be afraid to mix and match. The best retrieval systems aren’t pure; they are pragmatic.

Be well and build good systems.