I first built RAG solutions 1.5 years ago. Took a gap. Recently came back to study and implement it again.
One thing became clear: There’s no one-size-fits-all RAG architecture. I studied six different techniques and implemented some in my side projects. You choose based on what you need to solve.
The Techniques I Explored
Hypothetical Prompt Embeddings (HyPE) - Instead of embedding raw text, you generate hypothetical questions for each chunk during indexing. Retrieval becomes question-to-question matching. No runtime overhead. Clever approach.
Context Enrichment Window - Standard vector search returns isolated chunks. This technique fetches neighboring chunks too. Simple idea but makes results much more coherent.
Semantic Chunking - Splits text at natural breakpoints instead of fixed character counts. Uses embeddings to find where meaning shifts. LangChain has this built in with different breakpoint strategies.
Contextual Compression - After retrieval, an LLM extracts only the relevant parts from each document. Less noise for the final generation step. More tokens used but better focus.
Corrective RAG - This one impressed me most. It evaluates retrieved documents for relevance. If score is low, it searches the web instead. If ambiguous, it combines both sources. Dynamic and adaptive.
GraphRAG - Represents knowledge as an interconnected graph instead of flat chunks. Nodes are text chunks, edges show relationships between concepts. Uses a Dijkstra-like algorithm to traverse the graph during queries. Can visualize how the system finds answers. More complex to set up but powerful for documents with many interconnected concepts.
What Stood Out
Corrective RAG - The evaluation and correction loop felt right. Real queries do not always match what is in your knowledge base. Sometimes information is outdated. Sometimes the retrieval just misses.
Corrective RAG handles these cases gracefully. High relevance? Use the document. Low relevance? Search the web. Somewhere in between? Combine both. The system adapts instead of failing silently. This matches how humans actually research.
GraphRAG - The graph-based approach changed how I think about knowledge representation. Instead of treating documents as isolated chunks, you model relationships between concepts. The visualization aspect is powerful too. You can actually see how the system traverses information to find answers. More setup work but worth it for complex domains.
The Real Lesson
Choosing RAG architecture is a business decision, not just a technical one.
Each technique solves different problems:
- Need offline-first with no runtime LLM calls? Use HyPE
- Working with long documents where context matters? Use Context Enrichment
- Documents have natural sections and topics? Use Semantic Chunking
- Retrieved chunks have too much noise? Use Contextual Compression
- Knowledge base might be incomplete or outdated? Use Corrective RAG
- Documents have many interconnected concepts and relationships? Use GraphRAG
You can also combine them. Semantic chunking with context enrichment. HyPE with compression. The architecture should match your specific use case.
What I Learned
Studying these techniques changed how I think about RAG. Before, I just did basic chunk-and-embed. Now I ask different questions. What is the retrieval bottleneck? Where does the system fail? What trade-offs matter for this use case?
The field moves fast. New techniques keep appearing. But understanding the fundamentals helps you evaluate what actually matters versus what is just hype.
Still experimenting. Building small projects to test these ideas. The learning continues.