Prompt Engineering

Introduction In the fast-evolving world of software security, large language models (LLMs) are emerging as powerful allies for vulnerability researchers. Unlike traditional static analysis tools or manual code reviews, which often struggle with subtle logic flaws buried deep in complex codebases, LLMs can reason across vast contexts, spot patterns from training data, and simulate attacker mindsets. However, their effectiveness hinges on how we wield them. Overloading prompts with excessive scaffolding—think bloated agent configurations or exhaustive context dumps—paradoxically blinds models to critical “needles” in the haystack of code.[3] ...

Introduction The past few years have witnessed a dramatic transformation in how developers, product teams, and researchers interact with large language models (LLMs). In 2023–2024, prompt engineering—the art of crafting textual inputs that coax LLMs into producing the desired output—was the dominant paradigm. By 2026, however, the conversation has shifted toward agentic workflow orchestration: a higher‑level approach that treats LLMs as autonomous agents capable of planning, executing, and iterating on complex tasks across multiple tools and data sources. ...

Introduction Large language models (LLMs) such as GPT‑4, Claude, and LLaMA have demonstrated remarkable abilities to generate fluent, context‑aware text. Yet, their knowledge is static—frozen at the moment of pre‑training—and they lack a reliable mechanism for accessing up‑to‑date, structured information. Retrieval‑Augmented Generation (RAG) addresses this gap by coupling LLMs with an external knowledge source, typically a vector store of unstructured documents. While vector‑based RAG works well for textual retrieval, many domains (e.g., biomedical research, supply‑chain logistics, social networks) are naturally expressed as graphs: entities linked by typed relationships, often enriched with attributes and ontologies. Knowledge graphs (KGs) capture this relational structure, enabling queries that go beyond keyword matching—think “find all researchers who co‑authored a paper with a Nobel laureate after 2015”. ...

Introduction The rapid evolution of large language models (LLMs) has turned prompt engineering into a mainstream practice. Early‑stage developers often treat an LLM as a sophisticated autocomplete engine: feed it a carefully crafted prompt, receive a text response, and then act on that output. While this “prompt‑then‑act” loop works for simple question‑answering or single‑turn tasks, it quickly breaks down when we ask an LLM to operate autonomously—to plan, execute, and adapt over many interaction cycles without human supervision. ...

Large Language Models (LLMs) power tools like ChatGPT, revolutionizing how we interact with AI. This zero-to-hero guide takes you from foundational machine learning concepts to building, fine-tuning, and deploying LLMs, with curated link resources for hands-on learning.[1][2][3] Whether you’re a beginner with basic Python skills or an intermediate learner aiming for expertise, this post provides a structured path. We’ll cover theory, practical implementations, and pitfalls, drawing from top courses and tutorials. ...