RAG vs Fine-Tuning: Choosing the Right LLM Approach for Your Product
Both RAG and fine-tuning improve LLM performance on your specific use case — but they solve different problems. Here's how to choose.
AI / LLM
LangChain
LLM orchestration and agent frameworks for production AI applications
95K+GitHub stars
65+Vector store integrations
100+LLM providers supported
500+Pre-built tools
HOW WE USE IT
LangChain in our stack
We build LLM-powered applications using LangChain and LangGraph for orchestration, chaining, and agent architectures. From simple retrieval chains to complex multi-agent workflows, we architect systems that are maintainable, observable, and production-ready.
CAPABILITIES
What we deliver
- RAG pipeline design and implementation
- Multi-agent architectures with LangGraph
- LangSmith observability and tracing
- Custom tool and retriever integration
- Memory systems and conversation management
- Streaming responses and async chains
USE CASES
How we apply LangChain
Enterprise RAG
Production RAG systems over internal knowledge bases with evaluation frameworks and hybrid search.
Autonomous Agents
Tool-using agents that search the web, query databases, execute code, and call external APIs.
Multi-LLM Pipelines
Chains that route to different models (GPT-4/Claude/local) based on cost, latency, and capability requirements.
Engineering Stack
Built with the tools that matter
38 production-grade technologies — every one battle-tested in shipped products.
Frequently Asked Questions
Didn't find what you were searching for? Reach out to us at [email protected] and we'll assist you promptly.
Raw API calls are fine for simple prompts. LangChain pays off when you need RAG pipelines with retrieval, reranking, and context assembly; multi-step chains where outputs feed into other LLM or tool calls; agent architectures that use tools and maintain memory; and observability with LangSmith for tracing and evaluation. The framework handles prompt templating, output parsing, retrieval integration, and streaming — reducing the engineering effort for complex LLM workflows significantly.
A production LangChain system includes: a LangSmith tracing integration for full observability, async chain execution for concurrency, streaming response handling, retry and fallback logic for LLM API failures, a retrieval evaluation framework, and a CI/CD pipeline for prompt and chain versioning. We use LangGraph for stateful multi-agent workflows. Every production deployment includes monitoring dashboards and alerting on latency, error rates, and evaluation metric drift.
A production RAG system — ingestion pipeline, vector store, retrieval evaluation, LLM integration, and streaming API — typically takes 5-8 weeks end-to-end. Simple document Q&A systems can ship in 3-5 weeks. Complex multi-agent architectures with external tool integrations and approval workflows run 8-12 weeks. Retrieval quality tuning and evaluation framework setup account for roughly 40% of the total effort.
FROM OUR CLIENTS
Built with teams who ship
The team took our AI concept from whiteboard to production in 10 weeks. The architecture they designed handles 10x our expected load with no issues.
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