24.37 Use cases of Prompt Engineering, RAG, and Fine Tuning


Prompt Engineering, RAG (Retrieval Augmented Generation), and Fine Tuning are the concepts that may look confusing. Primarily, which problems can be solved by which one of these? What are the ideal use cases of these concepts? In this article we will go through the use cases of these. This is not an article on what these are. I assume you have a basic understanding of these concepts.

Prompt Engineering RAG Fine-Tuning PreTrain from scratch
ELI5 Close Book Exam without any preparation No preparation but Open Book Exam Close book exam with preparation Close book exam with curriculum preparation
Basic Structure - Priming
- Style
- Handling errors
- {Dynamic Content}
- Output formatting
Almost all of Prompt Engineering
Knowledge Base Context
A smaller version of a Pre Trained LLM - PreTrained LLM with a large corpus
Components LLM LLM + Knowledge Base Embeddings PreTrained LLM + Instruction Tuning A large corpus + lots of training resources
Flow Prompt -> LLM -> Response ~Offline:
Knowledge Base -> Embeddings

Prompt -> LLM -> Query -> Embeddings
-> Embeddings Response -> ReRank -> LLM
-> Response
Instruction-tuned data -> LLM

Prompt -> Fine Tuned LLM -> Response
A lot of data -> GPUs

Sentence completion
Online? Only Online ~Offline + Online Offline + Online Offline + ~Online
Cost $ $$ $$$ (it depends) $$$$$
Complexity Low Medium High Higher
Strength - Easy to work
- Rapid Prototyping
- To answer generic questions
- To generate generic texts
- To make generic tasks like summarization
- Connect external data sources
- Effective on a large context/KB
- Frequently updated dynamic content
- Proprietary information
- To respond in a specific style
- To decrease the prompt length
- A smaller model can perform the same as a larger one.
- Narrow down the scope of the output
- No unnecessary knowledge
- No copyright issue
Hallucination Can be confined to its context window. Low Medium
Training Kind of in Few Short Learning Embeddings need to be computed Extension of FSL with examples in a DB called,
Instruction tuned dataset
Training Duration N/A Real Time High depends on the size of the dataset and other parameters
Modify Output style βœ… ❌ βœ…
Apply Domain Knowledge ❌ βœ… βœ…
Limited to Context Window βœ… βœ… ❌


All these three are not mutually exclusive. Two or all of them can be combined to provide better output.


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Thoughts πŸ€” by Soumendra Kumar Sahoo is licensed under CC BY 4.0