Developing AI Models
1. Define Your Domain and Use Cases Clearly
First, you need to narrow the scope of expertise:
- What specific knowledge area?
- What tasks do you want the AI to perform expertly?
This clarity shapes the kind of data and examples you’ll feed it.
2. Teach the Model Through Interaction (Light Customization)
For models like ChatGPT, you can:
- Feed it structured examples — and say, “Use this structure for all future responses.”
- Refine responses iteratively — correct its tone, accuracy, or style over time.
Each correction acts like incremental fine-tuning through context learning. - Use “memory” features (like mine) to persist your domain rules and preferences.
This approach gradually makes the AI “sound” like an insider in your field.
3. Create a Domain Knowledge Base
You can build a reference library for the AI:
- Upload or link your articles, transcripts, or notes.
- Use them as grounding context:
- The AI can learn style, vocabulary, and tone by analyzing these texts.
If you were building a specialized AI assistant, you’d store this in a vector database (like Pinecone or FAISS) for retrieval-augmented generation (RAG).
4. Fine-Tune or Train the Model (Advanced Customization)
If you have technical or developer access:
- Collect domain data — scripts, reports, Q&A examples, audience interactions.
- Fine-tune the model (if allowed by the platform) so it internalizes your domain style and knowledge.
- Evaluate outputs continuously with subject matter experts (SMEs) to refine.
5. Add Domain Rules and Guardrails
Set explicit rules or style guides Tone, forbidden phrases: jargon, filler words and format.
6. Continuous Feedback and Evaluation
Keep the AI improving:
- Have it produce outputs regularly.
- Score those outputs for relevance, tone, and insight.
- Retrain or refine based on feedback loops.
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