RAG vs Fine-Tuning vs Alternatives

1. RAG (Retrieval-augmented generation)

Overview:

• Combines retrieval and generation.
• Retrieves relevant documents or chunks from a knowledge base.
• The model then generates a response based on both the prompt and the retrieved content.

Cons:

• Depends on retrieval quality.
• Slightly higher latency.
• Requires vector DB setup.

Use Cases:

• Chatbots with knowledge bases.
• Real-time support systems .

* Assistants with dynamic information.

2. Fine-Tuning

Overveiw:

• Update model weights with domain or task-specific data.
• Trains the model to behave specifically for your use case.

Types:

• Full fine-tuning
• Parameter-efficient (LoRA, QLoRA)

Cons:

• Expensive in terms of compute.
• Harder to update ( Need more GPU and datasets ).
• Risk of overfitting.

Use Cases:

• Legal or medical assistants
• Summarization of company documents
• Enterprise chat models

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3. Alternatives to RAG and Fine-Tuning

3.1 Prompt Engineering

• Design prompts to guide model behavior.
• Use zero-shot or few-shot learning.

Pros:

• Fast and low-cost
• No retraining needed

Cons:

• Limited in complexity and flexibility

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3.2 Instruction-Tuned Models

• Use LLMs already fine-tuned on instructions.
• E.g., GPT-Instruct, Mistral-Instruct, Zephyr, LLaMA-2-chat

Pros:

• High generalization
• Works well with structured prompts

Cons:

• Cannot customize deeply

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Summary Table

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