Langchain vs RAG: Choosing the Right LLM Architecture
Langchain vs RAG: Choosing the Right LLM Architecture
April 30, 2025
When building apps with large language models, choosing the right architecture is key. At Essid Solutions, we help startups and enterprises decide between Langchain chains and retrieval-augmented generation (RAG) to power their AI use cases.
π§ Whatβs the Difference?
- Langchain (Agent/Chain-based):
- Orchestrates calls to LLMs and tools
- Enables dynamic workflows (e.g., answer + take action)
- Great for agents, multi-step tools, or logic branching
- RAG (Retrieval-Augmented Generation):
- Enriches LLM prompts with external documents
- Vector search provides accurate, real-world grounding
- Ideal for internal knowledge bases, PDFs, or private content
βοΈ When to Use Each
| Use Case | Best Approach |
|---|---|
| Internal knowledge bot | RAG |
| Multi-tool agent (e.g., planner) | Langchain |
| PDF or document Q&A | RAG |
| Complex workflows (e.g., CRM bot) | Langchain |
| Customer support chatbot | RAG + Langchain |
Many use both: Langchain to control logic, RAG to supply data.
π§ Tools & Components
- RAG Stack: Langchain / LlamaIndex + Pinecone / ChromaDB + OpenAI / Cohere
- Langchain Tools: Agents, Chains, Memory, Callbacks
- Vector Stores: ChromaDB, Weaviate, Pinecone
- Backends: FastAPI, Node.js, Firebase Functions
πΌ Use Case: Customer Support Assistant
A SaaS client needed an AI chatbot that answers user questions using internal documentation. We:
- Indexed their docs using ChromaDB
- Used RAG with Langchain to enrich responses
- Built a React + FastAPI app with usage logging
Result: 65% reduction in support tickets and 90% user satisfaction with the bot.
π Not Sure Which to Choose?
We help you design and implement the right AI architecture for your product.
π Book an AI architecture session
Or email: hi@essidsolutions.com