Transfer Learning for LLMs
Transfer Learning for LLMs
What is Transfer Learning?
Transfer learning means taking a model pre-trained on a large general dataset and adapting it to a specific task or domain. Instead of training from scratch (which costs millions of dollars), you leverage the knowledge already learned.
The LLM Training Pipeline
- Pre-training: Train on massive text corpus (web pages, books, code). Learns language understanding. Cost: $1M-$100M+
- Supervised Fine-Tuning (SFT): Train on curated instruction/response pairs. Learns to follow instructions. Cost: $100-$10K
- RLHF/DPO: Align with human preferences using reinforcement learning. Learns what humans consider good responses. Cost: $1K-$100K
When to Fine-Tune vs. Prompt
| Approach | Best When | Limitations |
|---|---|---|
| Prompt Engineering | Quick iteration, diverse tasks, small datasets | Limited by context window, higher per-query cost |
| RAG | Factual grounding, frequently changing data | Retrieval errors, latency overhead |
| Fine-Tuning | Consistent style/format, domain specialization, latency-sensitive | Requires training data, risk of catastrophic forgetting |
Fine-Tuning Use Cases
- Consistent output format (always valid JSON, specific schema)
- Domain-specific language (medical, legal, financial terminology)
- Style matching (brand voice, documentation style)
- Distillation (training a smaller model to mimic a larger one)
🌼 Daisy+ in Action: Transfer Through Context
Rather than fine-tuning foundation models, Daisy+ takes a transfer learning approach through careful prompt engineering and tool use — the base Claude model's capabilities are "transferred" to ERP-specific tasks through context, system prompts, and structured data access via MCP. This means Daisy+ gets smarter with every Claude model upgrade, without retraining anything.
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