Prompt Engineering Fundamentals
Prompt engineering is the design of inputs to steer LLM behavior. It has evolved from "trial and error" to a disciplined architectural practice, with frameworks like DSPy treating it as a compilation problem rather than a writing exercise.
Few-Shot and In-Context Learning (ICL)
In-Context Learning (ICL) is the ability of an LLM to learn a new task simply by seeing examples in the prompt, without any weight updates. Maximizing ICL efficiency is a key lever for prompt stability.
Chain-of-Thought (CoT)
Chain-of-Thought (CoT) is the technique of encouraging an LLM to generate intermediate reasoning steps before providing a final answer. It has evolved from a simple prompt phrase into the core architectural feature of reasoning models (o1, DeepSeek-R2, Claude Opus 4.7 with extended thinking, GPT-5.5 with extended thinking).
Tree-of-Thought (ToT)
Tree-of-Thought (ToT) is an advanced prompting architecture where a model explores multiple reasoning paths, evaluates them, and "backtracks" if a path leads to a dead end. It is the blueprint behind modern autonomous research agents.
Context Engineering
Context engineering is the science of filling the LLM's finite "working memory" with the most valuable tokens. With context windows now reaching 1M+ tokens (Claude Sonnet 4.6, Gemini 3.1 Pro, GPT-5.5) and models gaining Extended Thinking, the focus has shifted from "fitting data" to "ranking relevance" and "managing compute budget."
Structured Generation
Structured Generation is the process of forcing an LLM to produce output in a machine-readable format (JSON, YAML, CSV) with 100% reliability. The discipline has moved from "prompt-based requests" to "engine-level constraints."
Prompt Optimization (DSPy)
Prompting has moved from the "Hand-tuning" era to the "Programmatic" era. DSPy (Declarative Self-improving Language Programs) is the de-facto standard for building robust LLM pipelines where prompts are optimized automatically by algorithms. The 3.x line (DSPy 3.1.3 shipped Feb 5, 2026, with point releases through May 2026) introduces tighter integration with reasoning-native models and a cleaner async runtime.
Prompt Injection and Defense
As LLMs become the "operating system" for applications, Prompt Injection is the new "SQL Injection." It is the #1 LLM risk in the OWASP LLM Top 10, and modern defense treats it as an architectural concern, not just a prompt-writing one.