Search Your AI Chat History: rl4_ask Turns Conversations into Knowledge

The Problem: Your Best Ideas Are Buried in Chat History

You had a great conversation with Cursor three weeks ago about caching strategy. You remember the AI suggested something clever. But where is it?

Cursor doesn't have a search function for chat history. You scroll through dozens of threads, reading titles that all say "Help me with..." until you give up and start the conversation from scratch.

Sound familiar? You're not alone. Context loss costs developers hours every weekContext loss costs developers hours every week/cursor/blog/hidden-cost-context-loss-ai-development.

The Solution: rl4_ask

`rl4_ask` is a natural language search engine for your development history. It's like having Perplexity, but for your own codebase conversations.

Ask a question in plain English. Get a cited answer with sources.

You: "Why did we choose Redis for caching?"
rl4_ask: "Based on the conversation from Feb 3 [chat_history.jsonl L142],
you evaluated Redis vs Memcached. The decision [dec-cache-strategy] chose
Redis because of its persistence options and pub/sub support for
cache invalidation across services."

Every claim is backed by a citation. No hallucination, no guessing — just your actual history.

How rl4_ask Works Under the Hood

1. Automatic Capture

RL4 continuously captures your Cursor chat history into `chat_history.jsonl`. Every message, every thread — automatically. No manual export needed.

2. RAG Pipeline

When you ask a question, `rl4_ask` runs a full RAG (Retrieval-Augmented Generation) pipeline:

Your question
  ↓
Intent detection (why/how/what/when/who)
  ↓
Entity extraction (files, dates, tags)
  ↓
Synonym expansion (Redis → cache, caching, store)
  ↓
BM25 + RRF search across all sources
  ↓
Recency boost (recent results ranked higher)
  ↓
Cited answer with sources

3. Multi-Source Search

`rl4_ask` doesn't just search chats. It searches across:

**Chat history** — your Cursor conversations
**Evidence** — captured facts and file activity
**Timeline** — your project's chronological journal
**Decisions** — structured architectural decisions
**CLI history** — commands you ran, with results

One question, all sources, one answer.

Real Examples

"What did we decide about authentication?"

Based on decision [dec-auth-strategy, 2026-01-15]:
- Chose JWT over session cookies
- Reason: stateless, works with microservices
- Confidence: high
- Related chat from Jan 14 [thread-abc123] discussed
the trade-offs in detail

"When did I last work on the payment module?"

Last activity on payment module: Feb 10, 2026
[activity.jsonl] Files modified:
- src/payments/processor.ts (3 saves)
- src/payments/webhook.ts (1 save)
[chat_history.jsonl] Thread "Payment webhook retry logic"
from Feb 10 discussed error handling

"What npm commands failed recently?"

[cli_history.jsonl, Feb 12]:
- npm run build → exit code 1 (TypeScript error in auth.ts:42)
- npm test → exit code 1 (3 failing tests in payment.spec.ts)
Both were resolved in the same session [session-feb12-pm]

rl4_ask vs search_context vs search_chats

RL4 offers three search tools. Here's when to use each:

| Tool | Best For | Returns |

|------|----------|---------|

| `rl4_ask` | Questions ("Why did we...?") | Cited natural language answer |

| `search_context` | Keyword search with filters | Raw chunks with citations |

| `search_chats` | Chat-only search | Chat chunks with thread IDs |

Use `rl4_ask` when you want an answer to a question.

Use `search_context` when you want raw data with specific filters (date range, tags, source type).

Use `search_chats` when you specifically need chat messages only.

Using rl4_ask in Cursor IDE

Since `rl4_ask` is an MCP tool, you use it naturally in Cursor conversations:

You: Ask rl4 why we switched from Sequelize to Prisma

Cursor calls `rl4_ask` behind the scenes and returns the cited answer directly in the chat.

Power Features

Filter by source:

Search only my chat history for the caching discussion
→ uses source="chat" filter

Filter by date:

What did I work on last week?
→ uses date_from/date_to filters

Filter by tag:

Show me all architecture decisions
→ uses tag="ARCH" filter

Quality Guarantee: rl4_guardrail

Every answer from `rl4_ask` can be validated by `rl4_guardrail`:

Checks that the response contains at least one citation
Ensures answers are grounded in actual evidence
No hallucinated "I think you mentioned..." responses

This is proof-backed development history. If `rl4_ask` says you decided something, it points to exactly where and when.

Getting Started

Prerequisites

RL4 extension installed in Cursor IDE
At least one snapshot generated (to populate `.rl4/` data)
MCP server connected (automatic with the extension)

Your First Search

Open a Cursor chat
Type: "Ask rl4: What have I been working on recently?"
Get a cited summary of your recent development activity

Build the Habit

The more you use Cursor with RL4 capturing in the background, the richer your searchable history becomes. After a week, you'll have hundreds of chat messages, dozens of file events, and multiple decisions — all searchable.

From Scattered Chats to Searchable Knowledge

Without `rl4_ask`:

Scroll through old threads hoping to find something
Re-ask questions you already solved
Lose architectural decisions in chat noise
Spend 10+ minutes searching for one conversation

With `rl4_ask`:

Ask a question, get a cited answer in seconds
Never re-solve a problem you already solved
Every decision is indexed and searchable
Your chat history becomes a knowledge base

What's Next

Your AI conversations are a goldmine of decisions, solutions, and insights. Stop letting them disappear.

Learn how to export your chat historyexport your chat history/cursor/blog/export-cursor-chat-history-complete-guide for backup, explore all 14 MCP tools14 MCP tools/cursor/blog/rl4-mcp-tools-cursor-complete-guide available, or understand why automated context beats manual noteswhy automated context beats manual notes/cursor/blog/why-rl4-over-manual-summaries.

**Try RL4 for Cursor IDE**Try RL4 for Cursor IDE/cursor/form — turn your AI chat history into searchable knowledge. Every conversation captured, every answer cited.