Instead of letting AI answer immediately (and make stuff up), I force it to: (1) plan what it will say, (2) search for real facts based on that plan, (3) then write the answer using only those facts. Result: way less hallucination, shorter answers, same quality, half the cost.

The Problem Everyone Knows

AI lies. Confidently. It invents citations, makes up statistics, and fabricates facts that sound perfectly reasonable.

Why? Because LLMs don't "know" anything. They predict the next most likely word. When they don't know the real answer, they generate the most plausible-sounding one instead. That's hallucination — and it's not a bug, it's how the system fundamentally works.

Every LLM does this. ChatGPT, Claude, Gemini, Llama — all of them. The architecture itself is the cause.

Why Current Fixes Don't Fully Work

RAG (Retrieval-Augmented Generation): Searches for info, dumps it all into the AI, and hopes it picks the right parts. Problem: the search is untargeted. You get a pile of documents and the AI still has to figure out what's relevant.

Chain-of-Thought: Makes AI "think step by step." Helps with reasoning, but doesn't stop it from confidently stating false facts.

"Just tell it to be accurate": Doesn't work. The AI doesn't have an internal accuracy meter.

What I Built: A 3-Step Pipeline

Step 1 — Plan Before Speaking

Before the AI writes any answer, I force it to create a structured outline first. Just the key topics it plans to cover — nothing more.

Think of it as pinning points on a map before starting a road trip.

This outline is tiny — about 15 tokens. The AI now knows WHERE it's going but hasn't written anything yet.

Step 2 — Search Between the Pins (not an AI call)

Here's the key insight: I take each point from the outline and search for real facts about each one. Web search, database query, whatever data source you have.

This is the "drone" — I send a drone to photograph every pin on the map before the car starts driving. Now I know what's actually at each point, not what the AI thinks is there.

This step doesn't use the LLM at all. Zero AI tokens. Just targeted search.

Step 3 — Write Using Real Facts Only

Now the AI gets: the outline (from step 1) + the real facts (from step 2). It writes the answer by filling in between the pins using verified information.

The AI doesn't need to guess anymore because the facts are right in front of it.

Results (Tested in Real Conversations)

I tested this with the same question answered 3 ways:

Method	Output Tokens	Conclusion	Hallucination Risk
Normal AI (no control)	~650 tokens	"It depends, decide yourself"	High
My system without search	~100 tokens	Same conclusion	Lower
My system with search	~100 tokens	Same conclusion + real facts	Much lower

Same answer quality. 85% fewer tokens. Real facts instead of guesses.

Cost Comparison (1M questions/month, Claude Haiku)

Method	Monthly Cost	Hallucination
Normal	$3,350	High
Plan + Expand (no search)	$500	Lower
Plan + Search + Expand	$740	Much lower

The search version costs $240 more than the no-search version, but $2,610 less than normal — and hallucination drops significantly.

Why This Is Different From RAG

	Normal RAG	This System
Search timing	Before AI thinks	After AI plans
Search strategy	Based on raw question (broad)	Based on outline (targeted)
Relevance	Dumps everything, hopes for the best	Every piece maps to a pin
Output control	None — AI writes however long it wants	Outline + expansion rate = predictable

Normal RAG is fishing with a net. This is fishing with a spear.

The Philosophy Behind It

This came from an observation about how humans think vs how AI thinks:

• Humans plan before speaking. We outline our thoughts, recall relevant facts, then construct our answer.
• AI just starts talking and figures it out along the way.

I'm not trying to make AI human. I'm trying to make it better at being AI — by giving it a structure that compensates for its natural weaknesses.

The principle: slow down to see more. Not slower processing — smarter preparation.

What's Next

The system has 4 layers that work together:

1. Compressed input — structured encoding that cuts input tokens 78%
2. Repeated reading — AI reads critical data twice for deeper understanding
3. Plan before answering — outline first, then expand within limits
4. Search between pins — targeted facts for every point in the outline

Each layer is optional. You pick the level of accuracy you need for each task:

• Quick question? Layer 1+3 (cheapest)
• Important analysis? Layer 1+2+3
• Must not hallucinate? All 4 layers

No one-size-fits-all. Just the right tool for the right job.

Built by a solo developer in Thailand. Not affiliated with any AI lab. Just trying to make AI more honest without making it more expensive.

Happy to answer questions about the technical details.
r/OpenAI r/claude r/GeminiAI r/token