1. Define Your Use Case Clearly
- What AI features do you want? (e.g., text summarization, question answering, code generation, chatbots, document analysis)
- What’s your data domain? (enterprise docs, customer chats, domain-specific terminology)
- How will users interact? (API, UI, batch processing)
2. Choose Your Approach: Fine-tuning vs. Prompt Engineering
Option A: Use Pretrained LLMs with Prompt Engineering (Most Common & Fast)
- Use existing models like OpenAI’s GPT, Anthropic, Cohere, or open-source models (Llama 2, Falcon).
- Design prompts to guide the model for your tasks without retraining.
- Examples: Provide examples in prompts, set instructions, use few-shot learning.
Option B: Fine-tune a Pretrained Model on Your Data (Domain Adaptation)
- Take a base LLM and fine-tune it on your enterprise-specific text.
- Requires labeled data or relevant corpora.
- Improves model accuracy on your domain.
- Use frameworks like Hugging Face Transformers, OpenAI’s fine-tuning API, or tools like LangChain.
3. Prepare Your Data
- Collect and clean enterprise-specific data (documents, emails, logs).
- Format it properly: e.g., pairs of input-output if supervised fine-tuning.
- Ensure privacy and compliance with your company’s policies.
4. Choose Your Tech Stack and Model
- Cloud APIs: OpenAI, Azure OpenAI, Cohere, AI21 Studio — no infrastructure needed.
- Open-Source Models: Llama 2, Falcon, GPT-J, GPT-NeoX — run on your hardware or cloud GPU.
- Fine-tuning tools: Hugging Face Transformers, OpenAI CLI.
5. Fine-tuning (if applicable)
- Use small-scale fine-tuning with techniques like LoRA (Low-Rank Adaptation) to reduce resource needs.
- Train on your labeled data, validate performance.
- Monitor overfitting and data quality.
6. Integration into Your App
- Wrap your model calls or API calls into microservices.
- Build interfaces for querying the model.
- Add caching, rate limiting, and logging for reliability.
- Secure access with authentication and data encryption.
7. Testing and Evaluation
- Test AI outputs for accuracy, bias, and relevance.
- Gather user feedback and iterate.
- Monitor performance and costs.
8. Continuous Improvement
- Collect new data from usage to improve models.
- Regularly retrain or update your prompt strategies.
- Stay updated with new LLM releases and methods.
Tools & Resources
- Hugging Face: Fine-tuning, datasets, models (huggingface.co)
- OpenAI API: Easy access to powerful LLMs
- LangChain: Framework to build LLM apps (langchain.com)
- Weights & Biases / MLflow: Experiment tracking
- Cloud Providers: AWS Sagemaker, Azure ML, GCP AI Platform for managed training
Summary
| Step | What to Do |
|---|---|
| Define use case | Clarify AI feature goals & domain |
| Choose approach | Prompt engineering or fine-tuning |
| Prepare data | Collect, clean, format |
| Pick model/stack | Pretrained APIs or open-source + fine-tuning |
| Train or prompt design | Fine-tune if needed, otherwise craft prompts |
| Integrate | API/microservice + app integration |
| Test & monitor | Validate outputs, get feedback, adjust |
| Improve | Iterate with new data and models |
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