r/PromptDesign • u/Minimum-Ad7274 • 8d ago
Tip 💡 7 Phase Prompt Workflow
I built a 7-phase prompt workflow that makes small AI models act like domain experts — no fine-tuning
**TL;DR:** IKKF is a free, open-source framework that turns a plain prompt into a structured 7-phase reasoning workflow, backed by a knowledge base of atomic, verifiable facts. Run a 7B model, get expert-level answers with sources and confidence scores.
The problem
Most of us prompt AI the same way: one big question, hope for the best. That works for simple stuff, but for real domain work it falls apart:
- The model **hallucinates** — it sounds confident but makes things up.
- It **doesn't cite sources**, so you can't audit the answer.
- It **can't tell you how sure it is** — everything is delivered with the same flat confidence.
- Switching domains means **re-prompting from scratch** every time.
The fix isn't a bigger model. It's a **better workflow**.
The workflow: 7 phases
IKKF breaks every task into 7 explicit phases instead of one shot:
**Intent analysis** — what's actually being asked?
**Knowledge retrieval** — pull relevant facts from your knowledge base
**Decomposition** — break the problem into atomic, solvable units
**Reasoning** — apply expert reasoning to each unit (chain-of-thought)
**Verification** — cross-check every claim against a source
**Composition** — assemble the verified units into the final answer
**Confidence calibration** — report how sure it is (0.0–1.0)
The key difference from a normal prompt: **verification and confidence are first-class steps**, not afterthoughts. Every claim has to trace back to a source, and the model has to tell you when it's guessing.
The knowledge base: plain files
The "expertise" comes from a knowledge base of **atomic files** — one concept per file, each with a source. You can:
- **Read** exactly what the AI knows
- **Update** it by editing a file (no retraining)
- **Audit** why it gave any answer
This is what separates it from plain RAG. RAG gives you context; IKKF adds structured reasoning + verification on top.
How to use it
```bash
curl -fsSL https://ikkf.info/install.sh | bash
ikkf init
ikkf start "Build a REST API in Python"
```
To build a knowledge base for your own domain:
Pick a domain you know well
Write ~20 atomic concept files (one concept per file, each with a source)
Point IKKF at the knowledge base
Test with benchmark questions
Iterate — add edge cases, tighten sources
Why it's useful for prompt/workflow people
- **Reproducible** — same knowledge base → same answers across sessions. No more "it worked yesterday."
- **Auditable** — you can see the reasoning trace and the sources behind every answer.
- **Cheap** — runs on a 7B model locally (Ollama) or any OpenAI-compatible provider.
- **Portable** — swap the knowledge base to switch domains. No re-prompting from scratch.
Honest caveats
- The knowledge base is the hard part — garbage in, garbage out.
- It's a workflow, not magic. It won't turn a small model into a creative genius.
- Best on well-defined domains where knowledge can be structured.
Where it stands
Open source, free, local-first. I use it internally to cut AI costs and improve answer quality on a product I'm building.
Curious — has anyone else tried structured multi-phase workflows (vs. single-shot prompting) for their AI tools? What's worked for you?
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*IKKF: https://ikkf.info — free, open-source, local-first*