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This path teaches you how to design prompts that don’t just generate answers — they reason, verify, adapt, and collaborate. You’ll learn how to build workflows that reduce errors, create consistency, and scale across complex tasks. Each guide combines theory with practice, so you’ll not only understand the techniques but also test them in small labs that make the difference visible. By the end, you’ll know how to turn a single model into a reliable system: one that can explain its steps, double-check its own work, and even coordinate multiple reasoning strategies before deciding on the best answer.
Beginner’s guide to reducing LLM hallucinations. Learn to spot weak answers, add source checks, and use confidence fields. Includes a lab on adding a verify-before-answering step to boost reliability.
This advanced guide teaches how to make LLM outputs polished and consistent. You will build a style guide in the system prompt, add a verifier checklist, and test style variants. Labs cover rewrite scaffolds, length control, style tokens, and polishing tactics.
Learn Retrieval Augmented Generation for small document sets using a clear librarian and writer model. Build a minimal RAG workflow from chunking to citations, connect LLMs to retrievers, format grounded prompts, and test answers in a hands-on mini lab.
Learn how to prompt models to plan, sequence, and safely execute API calls. Master the Plan → Validate → Execute → Verify loop with tool selection, schema checks, retries, safety gates, and post execution verification for reliable workflows.
Explore advanced reasoning with Chain of Thought. Learn self consistency to boost accuracy by sampling and voting, and least to most prompting to break problems into steps. Covers when CoT helps, cost and latency tradeoffs, and how to validate gains.
Explore structured decomposition for complex questions. Learn Self Ask to create explicit sub questions, Auto CoT to generate reasoning examples, and retrieval layers to ground steps. Covers when to use modular vs least to most, plus cost, caching, and reuse.
Learn verification loops that make LLM answers reliable. Build a plan and check pipeline with CoVe to generate and verify questions, add a SelfCheckGPT pass to flag risky text, and balance depth, latency, and confidence with clear prompts and trade offs.
Learn inference time self improvement for LLMs without fine tuning. Use Self Refine loops to draft, critique, and revise, add CRITIC verification, and Reflexion memory. Covers rubrics with LLM as Judge, evolving prompts with OPRO or APE, and safe decoding.
Learn structured reasoning with Tree of Thoughts. Build loops with beam width, scoring rubrics, and budget caps. See when ToT outperforms self consistency, explore graph style variants, and apply prompts with reliability tactics for better results.
Learn Skeleton of Thought for long form generation by planning an outline first and expanding under word or token budgets. Improve density, latency, and readability with practical prompts, parallel fill, budget tricks, and checks for coverage and accuracy.
Learn to control long form outputs with precision. Blueprint sections with word budgets, set density targets, and enforce style. Extend Chain of Density to multi section docs, add self checks for coverage and accuracy, and apply an editorial review rubric.
Learn how to make long prompts reliable instead of drifting. Use Decision Frames and anchors to surface key facts, cut length with constraint tables and compression, know when to shrink instructions vs data, and harden RAG with retrieval aware methods.
Learn how to make long prompts shorter and smarter. Understand why models forget the middle of context, how to front load constraints and tail load examples, and apply a shrink to fit checklist inspired by LLMLingua and lost in the middle research.
Learn how to build LLM query routers that balance cost, speed, and quality. Use the Decide → Probe → Verify → Escalate pattern with rule based checks, verifiers, and escalation rules, then advance to learned routers with calibration, safety, and logging.
Learn a practical rank then respond workflow. Generate diverse candidates with role, constraint, and structure variations, then use a judge prompt with a clear rubric to rank or fuse answers. Includes reusable proposers, bias aware ranking, and stop rules.
Learn Active Prompting, a loop that probes model uncertainty, adds one high leverage gold demo, and builds a compact few shot library. Use dispersion and stability checks, write transferable key step rationales, and rebuild exemplars to fix failures.
Learn to use a small stimulus slot to steer a model’s tone, evidence rules, and brevity without changing the main prompt. This guide explains Directional Stimulus Prompting, where to place it, how to phrase it, and how to sanity check adherence.
Learn automatic prompt optimization with APE and ProTeGi. Generate strong task instructions, refine them through critic, edit, and re score loops, and prototype multi branch prompts that route tricky inputs. Includes scoring, artifacts, and auditing.
Master ReAct agents from scratch with this step-by-step guide. Learn the Thought → Action → Observation pattern, enforce safety and quality checks, and build phase-aware agents ready for production. Includes a full system prompt, controller loop, typed tool definitions, and troubleshooting tips to help you ship auditable, efficient, and safe AI agents.
Hands-on guide to Program-of-Thought (PoT) and PAL. Learn how to generate Python programs per task, run them safely in a sandbox, and return clean results with compact traces. Covers rerun guards, Decimal/itertools usage, single-shot vs. self-consistency, sandboxing, cost vs. latency trade-offs, and a 5-minute mini lab.
- Comfort with beginner-level prompting (roles, clarity, structured outputs). - Basic familiarity with JSON or schema-based outputs. - No coding required, but some examples reference API-style workflows.
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Advanced