Basic RAG vs. Agentic RAG

At CrawlChat, our goal is simple: deliver accurate, helpful answers from your documentation. While traditional RAG (Retrieval-Augmented Generation) does a decent job, it struggles when questions are complex or nuanced.

That’s why we’re evolving our engine into what we call Agentic RAG—a more intelligent, step-by-step approach that reduces hallucinations and improves reliability.

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What Is Basic RAG?

Traditional RAG works like this:

1. User asks a question.
2. The system retrieves relevant documents.
3. It sends the question and the retrieved context to the LLM.
4. The LLM generates a response.

✅ Pros:

• Simple and fast.
• Works well for direct, fact-based questions.

❌ Cons:

• Prone to hallucinations, especially with vague or multi-part questions.
• The LLM may miss important parts of context.
• No internal reasoning—just one pass from input to output.

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Introducing Agentic RAG

Agentic RAG adds reasoning and multi-step planning into the mix. Here's how it works in CrawlChat:

1. Break Down the Question
   The AI first decomposes the user’s query into smaller, atomic questions.

2. Retrieve Context per Sub-question
   Each sub-question triggers its own focused context retrieval.

3. Answer Step-by-Step
   The system generates answers for each sub-question independently.

4. Summarize the Final Answer
   It then combines all the sub-answers into one cohesive response.

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Why Agentic RAG Matters

Let’s say someone asks a question on Discord about Remotion’s API. Basic RAG might retrieve a few paragraphs and guess an answer—sometimes wrongly.

With Agentic RAG, CrawlChat:

• Breaks the query into:

  • What is the API used for?
  • How do I configure it in project X?

• Finds precise documentation for each.

• Answers both clearly.

• Merges them into a single, accurate reply.

✅ Pros:

• Much higher accuracy.
• Reduced hallucination risk.
• Better handling of complex or vague queries.

❌ Cons:

• Slightly slower (multiple steps).
• More compute-intensive.
• Needs smarter orchestration (which we’re building).

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What We're Seeing So Far

We’ve tested Agentic RAG internally and with early users. Even moderately complex queries are now getting split and answered more reliably. Some cases go through three or four steps, depending on the complexity.

We're currently letting users try this manually and so far, the feedback is positive.

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Bonus Features We're Adding

Alongside smarter search, we’ve rolled out:

• Support Ticketing
  When AI can’t help, users can raise a ticket instantly.

• Analytics & Geo Insights
  Know where your users come from and what they ask.

This brings CrawlChat closer to becoming a full-stack customer support assistant.

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What’s Next?

Agentic RAG is just the beginning. We're working on refining workflows, making reasoning more efficient, and reducing latency.

If you’re building a knowledge base or supporting a developer community, now’s the perfect time to try the new CrawlChat experience.

👉 Try it now at CrawlChat.app