Artificial Intelligence
Integrating LLMs: Patterns That Hold Up in Production
Practical patterns for reliable, cost-efficient LLM features: prompt engineering, streaming, structured outputs, fallbacks and cost management.
·3 min read
Tags
LLM
14 articles · Page 1 of 2
Artificial Intelligence
Production RAG: From Retrieval to Accurate Answers
Build production RAG pipelines that retrieve real context and answer accurately: chunking, embedding models, vector search, re-ranking and evaluation.
·4 min read
Artificial Intelligence
Building AI-Powered Code Review Assistants with LLM APIs
Build a code review assistant that hooks into CI, uses LLM APIs to scan pull requests for bugs, style and security issues, and comments on the PR.
·4 min read
Artificial Intelligence
Building RAG Systems That Actually Work
A practical guide to production RAG that returns grounded answers: embedding strategies, vector database choice, chunking, reranking and evaluation.
·5 min read
Artificial Intelligence
Evaluating LLM Output: Metrics and Testing Frameworks
A practical guide to evaluation pipelines for LLM features: semantic similarity metrics, behavioral testing, regression detection and quality gates.
·4 min read
Artificial Intelligence
Prompt Engineering for Code Generation
A practical guide to prompts that produce reliable, production-quality code: structured prompting, few-shot examples, chain-of-thought and evaluation.
·6 min read
Artificial Intelligence
Building RAG Pipelines That Actually Work
A hands-on guide to reliable RAG systems: chunking strategies, embedding models, vector stores and the retrieval patterns that separate demos from production.
·6 min read
Artificial Intelligence
Fine-Tuning Language Models on Domain-Specific Data
Fine-tune language models on your domain with parameter-efficient LoRA and QLoRA: dataset preparation, training strategies and evaluation, on a small budget.
·5 min read
Artificial Intelligence
Retrieval-Augmented Generation: RAG Pipelines That Work
Build effective RAG pipelines in TypeScript: document chunking, embeddings, vector search tuning, prompt construction and retrieval-quality evaluation.
·5 min read
Artificial Intelligence
Prompt Engineering Patterns for Production Applications
Systematic prompt engineering for production LLM applications: structured output extraction, chain-of-thought, few-shot calibration, versioning and evals.
·5 min read
Artificial Intelligence
Building Evaluation Frameworks for LLM Applications
Design evaluation pipelines for LLM applications: automated metrics, human evaluation protocols, regression tests, prompt versioning and monitoring.
·5 min read
Artificial Intelligence
Building Production-Ready RAG Pipelines with LangChain
Design retrieval-augmented generation pipelines that go beyond demos: chunking strategies, embedding optimization, reranking and evaluation metrics.
·5 min read