Results for “Prompt Engineering”
8 matches across blog posts, projects, case studies and series.
Blog posts
Building an Automated AI Daily Brief: My Morning Workflow
Stop checking five different apps to start your day. Here’s how I built a Python-based system that fetches tasks, prioritizes them with GPT-4, and emails me a …
Feb 22, 2026Building State: A Deep Dive into LangChain Memory for Conversational AI
LLMs are stateless by default. To build a conversational agent that actually remembers, you need to manage context effectively. This guide covers implementing …
Feb 22, 2026Unmasking the Black Box: Mastering LangChain Callbacks for Debugging
Master LangChain callbacks to debug AI agents. Learn to build custom handlers, trace execution flows, and fix 'black box' LLM issues.
Feb 22, 2026Structured JSON Output from LLMs: Schema Injection, Pydantic and Retry Loops
Why LLM output breaks JSON parsers, and three patterns that fix it in AI agents: schema injection with TypeScript types, Pydantic validation and a self-healing…
Feb 22, 2026Building Long-Term Memory: Setting Up Vector Databases for Semantic Search in LangChain
LLMs have a context limit; your data doesn't. Learn how to implement vector databases in LangChain to enable semantic search and build robust RAG pipelines.
Feb 22, 2026Beyond Built-ins: Architecting Custom Tools in LangChain
Standard LangChain tools handle the basics, but production agents need to touch internal APIs and proprietary logic. Here is a deep dive into building robust, …
Feb 22, 2026Architecting Autonomous Agents: A Builder’s Guide to Complex Workflows
Moving beyond simple chatbots requires a shift in architecture. Here is how to engineer autonomous agents using ReAct loops, tool usage, and state management t…
Feb 21, 2026Engineering a Personal Prompt System: Library, Versioning, and Automated Tests
Stop guessing with your prompts. Learn how to build a centralized prompt library, version control your system instructions, and write a simple Python test harn…