Fractional CTO for LangChain
LangChain is a framework for building applications with large language models. We help companies implement RAG systems, AI agents, and complex AI pipelines effectively....
Expert LangChain Leadership
LangChain is a framework for building applications with large language models. We help companies implement RAG systems, AI agents, and complex AI pipelines effectively.
RAG MVP: 4-6 weeks, Complex agent: 8-12 weeks
$30,000-$100,000
Why Choose Us
- Rapid prototyping of AI applications
- Pre-built integrations with vector stores and LLMs
- Strong community and examples
- Composable architecture with LCEL
- Good tooling with LangSmith
Common LangChain Challenges We Solve
Choosing between LangChain abstractions and direct API calls
RAG system design and vector store selection
Agent architecture and tool design
Managing LLM costs across complex chains
Debugging and tracing complex workflows
Production deployment and scaling
Evaluation and testing AI outputs
Staying current with rapid framework evolution
LangChain Best Practices
Start simple and add complexity incrementally
Use LangSmith for tracing and debugging
Design proper chunking strategies for RAG
Implement proper error handling for LLM failures
Use streaming for better user experience
Build comprehensive evaluation pipelines
Consider LCEL for composable chains
Monitor costs and token usage
Complementary Technologies
Scaling Considerations
LangChain adds abstraction overhead. For production scale, consider optimizing or removing unnecessary abstractions.
Security Considerations
Sanitize inputs to prevent prompt injection, secure API keys, implement proper access control for tools/agents.
Performance Optimization
Use async operations, implement caching, optimize retrieval, minimize chain complexity in production.
Success Story
Series A legal tech startup
Challenge
Needed to build document analysis system that could answer questions about legal contracts accurately.
Solution
Fractional CTO designed RAG architecture with LangChain, implemented proper chunking for legal documents, built evaluation framework.
Result
System achieves 92% accuracy on legal Q&A (up from 65% with naive approach). Processing 10,000+ documents daily. Saved $500K+ in legal review costs.
Timeline: 2 months
Recommended Team Structure
LangChain requires AI/ML expertise. Typical: 1-2 ML engineers with Python proficiency.
LangChain Solutions by Industry
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