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 9 of 22Hybrid 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.
The RCAF Prompt Structure: A 4-Part Skeleton for Maintainable Prompts
RCAF is a 4-part prompt skeleton — Role, Context, Action, Format — that produces maintainable prompts by separating identity, background, task, and output shape.
The SurePrompts Quality Rubric: A 7-Dimension Framework for Scoring Prompts
A structured way to evaluate prompt quality across 7 dimensions, scored 1-5 each for a max of 35. Replaces 'this prompt feels off' with concrete scores you can act on.
Context Engineering: The 2026 Replacement for Prompt Engineering
How context engineering — the discipline of assembling what a model sees — replaced prompt engineering as the 2026 quality lever. Strategies, patterns, and trade-offs.
Prompt Engineering for Business Teams: Marketing, Sales, Engineering, Ops
How business teams prompt AI for real work — briefs, discovery, architecture reviews, SOPs. Function-specific patterns across marketing, sales, engineering, and ops.