Prompt Engineering for Agentic AI
You have probably spent time learning how to prompt AI well.
Dataloco
Building Vector Similarity Search in PostgreSQL with pgvector
Search works well when users know exactly what they are looking for, but it breaks down when intent is described in natural language.
Choosing the Right Agentic Design Pattern: A Decision-Tree Approach
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LLM Observability Tools for Reliable AI Applications
Large language models (LLMs) now power everything from customer service bots to autonomous coding agents.
Implementing Prompt Compression to Reduce Agentic Loop Costs
Agentic loops in production can be synonymous with high costs, especially when it comes to both LLM and external application usage via APIs, where billing is often closely related to token usage.
Implementing Permission-Gated Tool Calling in Python Agents
AI agents have evolved beyond passive chatbots.
The Roadmap to Mastering Tool Calling in AI Agents
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Implementing Statistical Guardrails for Non-Deterministic Agents
Non-deterministic agents are those where the same input can lead to distinct outputs across multiple runs.
Agentic RAG Explained in 3 Levels of Difficulty
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Effective KV Compression with TurboQuant
TurboQuant has recently been launched by Google as a novel algorithmic suite and library for applying advanced quantization and compression to large language models (LLMs) and vector search engines — an indispensable element of RAG systems.
Train, Serve, and Deploy a Scikit-learn Model with FastAPI
FastAPI has become one of the most popular ways to serve machine learning models because it is lightweight, fast, and easy to use.
AI Agent Memory Explained in 3 Levels of Difficulty
A stateless AI agent has no memory of previous calls.
Getting Started with Zero-Shot Text Classification
Zero-shot text classification is a way to label text without first training a classifier on your own task-specific dataset.
The Complete Guide to Inference Caching in LLMs
Calling a large language model API at scale is expensive and slow.
Python Decorators for Production Machine Learning Engineering
You've probably written a decorator or two in your Python career.
5 Techniques for Efficient Long-Context RAG
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How to Implement Tool Calling with Gemma 4 and Python
The open-weights model ecosystem shifted recently with the release of the <a href="https://blog.
Structured Outputs vs. Function Calling: Which Should Your Agent Use?
Language models (LMs), at their core, are text-in and text-out systems.
Beyond Vector Search: Building a Deterministic 3-Tiered Graph-RAG System
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The Roadmap to Mastering Agentic AI Design Patterns
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