Exploration · RAG
AI Engineering / RAG Lab
How can an LLM produce more useful answers when it is grounded in relevant external knowledge instead of relying only on its model knowledge?
- Python
- Embeddings
- Vector Search
- RAG
- LLMs
- Sentence Transformers
- FAISS
[ Ongoing exploration — not a production product. ]
01The problem
How can an LLM produce more useful answers when it is grounded in relevant external knowledge instead of relying only on its model knowledge?
02Context
This is my exploration space for understanding retrieval-augmented generation, embeddings, vector search and grounded LLM workflows.
The goal is to understand the engineering layer around LLM applications.
The engineering layer
03Ideation
Instead of treating an LLM as a standalone answer generator, I explored a retrieval-first architecture.
The system should retrieve relevant information first and then provide that information to the model as context.
Before
After
04Solution
The experiments focus on:
- Document ingestion
- Embedding generation
- Semantic retrieval
- Vector search
- Context construction
- LLM generation
- Grounded responses
05Architecture
Component 01
Documents
The external knowledge the model should be grounded in.
Hover or tap a component
06Prototype evolution
- 01
Idea
Explore whether external context improves LLM responses.
- 02
Prototype
Create embeddings and perform semantic retrieval.
- 03
Retrieval
Retrieve the most relevant information based on similarity.
- 04
Generation
Pass retrieved context to the LLM.
- 05
Evaluation
Compare grounded responses against responses without retrieved context.
07Key learning
The quality of an LLM workflow is not only about the model. It is also about the quality of the context that reaches the model.
Next exploration
AI-Assisted Engineering Workflows