AI Application Engineering
LangChain for structured AI application workflows.
We use LangChain to connect language models with tools, retrieval systems, application logic, and multi-step AI workflows.
Overview
LangChain provides building blocks for developing applications around language models, including model interaction, tools, retrieval, and workflow orchestration.
Strengths
LLM application architecture
Tool integration
Retrieval workflows
Prompt and model orchestration
Multi-step AI systems
Application integration
Use Cases
AI assistants
RAG applications
Tool-using agents
Knowledge systems
Research assistants
AI automation
Internal AI platforms
Why We Use It
We use LangChain when its orchestration and integration capabilities simplify the architecture of an application built around language models and external tools.
Where It Fits
We select technologies according to project requirements, architecture, scalability, maintainability, security, and long-term operational needs.
Engineering Approach
The goal is not to use a particular technology for its own sake. We use the tools that make technical and business sense for the problem.
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