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.
🧐 What’s 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
⚖️ 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.
🔧 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
💼 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.
📅 Not Sure Which to Choose?
We help you design and implement the right AI architecture for your product.
👉 Book an AI architecture session
Or email: hi@essidsolutions.com
