Overview
Vanik was an experimental AI platform designed to explore how Large Language Models could be combined with domain-specific knowledge to build a more useful agricultural assistant.
The core problem was simple:
A general-purpose LLM does not automatically have reliable access to domain-specific information.
Retrieval-Augmented Generation provided a way to address this.
RAG Architecture
The system follows the general RAG pipeline:
User Query → Embedding → Vector Search → Relevant Context → LLM → Response
Instead of relying entirely on the model's internal knowledge, relevant information is retrieved from an external knowledge base and provided as context to the model.
Vector Embeddings
Domain-specific information is transformed into vector representations.
When a user asks a question, the query is also embedded and compared against stored vectors to retrieve semantically relevant information.
This allows the system to retrieve relevant context even when the user's wording differs from the source material.
LangChain
LangChain was used to experiment with:
- Retrieval pipelines
- Prompt construction
- LLM orchestration
- Context injection
- Tooling around model interactions
The project provided hands-on experience with the architecture surrounding LLM applications rather than simply calling a model API.
Voice Interaction
The system also explored voice-based interaction using text-to-speech capabilities.
This introduced another layer into the AI pipeline:
Voice / Text Input → AI Processing → Generated Response → Speech Output
Engineering Lessons
Vanik was particularly valuable because it exposed several limitations of early LLM applications:
- Retrieval quality matters as much as generation quality.
- Poor context produces poor answers.
- Prompt design cannot compensate for missing information.
- Vector search requires careful chunking and indexing.
- AI systems need an architecture around the model.
These lessons later influenced my approach to building more production-oriented AI systems.