Use LangChain when the product needs multi-step LLM chains with conditional logic, RAG pipelines linking LLMs to custom knowledge bases, autonomous agents calling external tools, or managing conversation memory across sessions. Use the OpenAI API directly when the case involves a single prompt-response or simple chat interface, where LangChain's abstraction adds unnecessary complexity. Direct API use is simpler, cheaper to debug, and faster to iterate for straightforward LLM integration.
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Oleh focuses on EVM smart contract engineering and blockchain data pipelines, with ~3.5 years of commercial experience as a Blockchain Software Develo. He delivers production Solidity, TypeScript and Rust code for token flows... Read More
Nine years developing data science solutions across agritech, defence, fintech, and sports analytics. Built Quantum's entire DS department from scratch — hired over 30 people, created learning pathways, and mentored MSc and P... Read More
Pavlo mostly specialises in Backend development with a strong emphasis on quantitative analysis and data processing. With nine years of experience, he is proficient in Python, Node.js and JavaScript, utilising frameworks such... Read More
Victoriia is a skilled Flutter Developer with 4 years of experience in mobile application development. She specializes in frameworks such as Flutter, leveraging JavaScript, DART, and utilizes databases like MySQL and Firebase... Read More
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