AI Prompt Engineering Blog
Expert guides, tutorials, and insights to master the art of prompt engineering for ChatGPT, Claude, Gemini, and beyond.
Latest Articles
Page 10 of 23Claude Opus 4.8 Prompting Guide: How to Get the Most From Anthropic's Top Model (2026)
A working reference for prompting Claude Opus 4.8 — adaptive thinking, 1M context, prompt caching, tool use, and the patterns that actually move quality and cost.
Corrective RAG (CRAG): Grading Retrieved Docs Before You Generate
Corrective RAG adds a grading step between retrieval and generation — if confidence is low, the pipeline falls back to web search or query rewriting instead of hallucinating on weak context. A working walkthrough with the three-branch router.
DSPy: An Introduction to Programming Prompts as Functions (2026)
DSPy treats prompts as typed functions — Signatures, Modules, Optimizers — instead of strings to hand-tune. This guide covers when DSPy helps, when it doesn't, and how to think about adopting it.
GraphRAG: When Knowledge Graphs Beat Chunk-Based Retrieval
GraphRAG builds a knowledge graph from the source corpus and uses its structure as retrieval context. This tutorial walks through the pipeline, where it wins over chunk-based RAG, and where it does not pay for itself.
Hybrid Search: Combining BM25 and Vector Retrieval for Production RAG
Hybrid search combines BM25 keyword scoring with vector similarity and fuses the rankings — the practical default for production RAG because real user queries come in both styles. This tutorial walks through the fusion strategies, weight tuning, and failure modes on a hypothetical e-commerce support bot.
HyDE Retrieval: Generating Hypothetical Answers to Improve Vector Search
HyDE (Hypothetical Document Embeddings) asks the model to draft a fake answer first, then retrieves against that. This tutorial walks through why it helps, when it hurts, and how to tune it on a hypothetical medical-literature corpus.
Least-to-Most Prompting: A Worked Example for Compositional Tasks
Least-to-Most decomposes a hard problem into easier sub-problems, solves them in order, and uses each result as input to the next. This tutorial walks through it end to end on a compositional reasoning task.
LLM-as-Judge: A Practical Guide to Automating Prompt Evaluation (2026)
How to use an LLM as an evaluator — rubric-based scoring, pairwise comparison, bias mitigation (position, verbosity, self-preference), and when to trust the judge's output.
Program-of-Thoughts Prompting: A Worked Example for Numerical Reasoning
Program-of-Thoughts separates language reasoning from arithmetic by generating code the model can execute. This tutorial walks through a revenue-forecast example end to end — prompt, code, execution, result.
RAGAS Evaluation: A Walkthrough for Quantifying RAG Quality
RAGAS measures RAG systems across 4 metrics — faithfulness, answer relevance, context precision, and context recall. This tutorial walks through each metric on a hypothetical customer-support RAG system.
10 RCAF Prompt Templates for Everyday Business Tasks
Copy-pasteable RCAF-structured (Role · Context · Action · Format) prompt templates for weekly standups, sales emails, meeting notes, competitor briefs, and 6 more recurring business tasks.
Reranking Retrieval Results: A Cross-Encoder Walkthrough
Bi-encoder similarity hits a ceiling around the top of the result list. This walkthrough shows how to add a cross-encoder reranker to a RAG pipeline, what the latency budget looks like, and which reranker families make sense in 2026.
Scoring a Customer Service Prompt with the SurePrompts Quality Rubric: A Worked Example
End-to-end walkthrough applying the 7-dimension SurePrompts Quality Rubric to a customer service prompt — from 9/35 baseline to 31/35 production-ready.
Self-Ask Prompting: A Guide to Decomposing Multi-Hop Questions
Self-Ask prompting makes the model ask and answer its own sub-questions before the final answer. Shown on multi-hop reasoning and research-assistant tasks with concrete prompt templates.
Semantic Router: Embedding-Based Routing Without Calling an LLM
A semantic router classifies incoming queries by comparing embeddings against a small set of labeled reference utterances per route. Faster, cheaper, and more deterministic than asking an LLM to route — this walkthrough shows how to build one and when to fall back to an LLM.
Step-Back Prompting: A Worked Example for Knowledge-Intensive Reasoning
Step-Back prompting asks the model to generate the general principle or abstraction before answering the specific question. This tutorial walks through it on physics, finance, and SQL examples.
The Agentic Prompt Stack: 6 Layers for Designing Prompts That Run Agents
The Agentic Prompt Stack organizes agent prompts into 6 layers — Goals, Tool permissions, Planning scaffold, Memory access, Output validation, Error recovery — so failures map to a specific layer to fix.
The Context Engineering Maturity Model: 5 Levels From Static Prompts to Orchestrated Systems
A 5-level maturity model for context engineering, from static hand-written prompts (L1) to multi-source orchestration with semantic caching and evaluation loops (L5). Self-assessment tool for teams.