Articles by John Godel

Unlock predictably great AI outputs! This article dives into prompt engineering's decoding discipline, focusing on sampling policies and sectioned generation. Learn how to stabilize latency, lower costs, and boost first-pass success by mastering top-p, temperature, stop sequences, and strategic sectioning. Optimize your AI workflows without retraining models! Discover practical defaults and concrete presets for various content types.

Transform LLM prompts into robust contracts for predictable, auditable AI systems. Learn how to define scope, structure output, enforce decision rules, manage evidence, and mediate tool use. Reduce risk, cut costs, and ensure reliable behavior by treating prompts as verifiable specifications. Versioning, testing, and validation are key!

GSCP-12 formalizes a production framework for LLM features, defining the Full-Stack Prompt Engineer role. It introduces a reference architecture using prompts as contracts and context as auditable claims. This model-agnostic approach ensures grounded, safe, fast, and cost-efficient LLM routes with measurable gates for regression detection and AI-specific validations.

Discover the Full-Stack Prompt Engineer (FSPE): a unified role blending prompt and context engineering for reliable AI features. Learn how FSPEs build governed, testable AI systems with versioned contracts, claim packs, and robust validation. Master the skills for safe, efficient, and auditable AI development, ensuring quality, speed, and cost-effectiveness from user ask to grounded answer. Embrace the future of AI with FSPE!

Discover the Full-Stack Prompt Engineer (FSPE): a unified role mastering both prompt and context engineering for AI product success. Learn how FSPEs build repeatable, governed AI features with versioned artifacts, ensuring grounded, safe, and efficient outputs. Explore responsibilities, artifacts, and skills to elevate your AI development process and deliver dependable AI products. This approach emphasizes testability, auditability, and measurable outcomes.

Transform your generative AI pipeline into a distribution powerhouse! Learn how to package content for multi-channel delivery, implement A/B testing frameworks, and close the loop with outcome data for continuous improvement. Discover strategies for channel adaptation, experimentation, and governance to maximize impact and minimize risk. Unlock faster shipping, higher lift, and lower cost per outcome with a governed, high-velocity content engine.

Optimize generative AI pipelines for cost and latency. Learn to define budgets, choose efficient decoder policies, implement strategic caching, and batch requests effectively. Discover how to measure $/accepted output, reduce repair loops, and route requests to the right model for maximum efficiency and quality. Implement these strategies to make your generative stack faster and cheaper.

Ensure brand safety and legal compliance in generative AI with policy-driven guardrails. Learn to encode rules into prompts, validators, and logs for enforceable limits on AI-generated content. Discover how to manage claim boundaries, enforce brand tone, and maintain auditability for confident scaling across regions and channels. Implement a robust system for reproducible and compliant AI outputs.

Master generative AI evaluation with golden sets, constraint pass-rate (CPR), self-consistency checks, and live canaries. Learn to define 'good' with policy JSON, track key metrics like CPR and time-to-valid, and implement robust release gates for faster, safer, and cheaper deployments. Move beyond subjective assessments and embrace data-driven quality assurance for your GenAI systems. Discover practical strategies for identifying and fixing failures, optimizing costs, and ensuring consistent performance.

Modular RAG blends generative AI with retrieval for content creation, ensuring on-brand, factually sound outputs. It uses policy-aware retrieval, atomic claims, and sectioned generation to cite evidence without disrupting creative flow. Validators enforce schema, citation coverage, and freshness, creating auditable and repeatable content production.

Scale content creation with programmatic creativity! Learn to build a system of reusable templates, parameterized prompts, and pluggable generators. This article outlines the architecture, artifacts, and metrics for generating high-quality, on-brand content at scale, reliably, cheaply, and fast. Move beyond single prompts and unlock efficient content production.

Learn how to control generative AI tone, persona, structure, and brand voice without fine-tuning using style frames, lexicons, constraints, and validation loops.

Unlock the power of generative AI! Learn how to control text generation without finetuning. Master decoding strategies like top-p sampling and temperature scaling. Discover format-first prompting, constraints, and validation techniques for on-brand, high-quality output. Improve your AI content today!

This article clarifies the relationship between prompt engineering and context engineering, emphasizing that context engineering is a crucial component supporting prompt engineering, not a replacement. It details how to build robust LLM applications by defining prompt contracts first, then constructing context pipelines to deliver admissible evidence, ensuring accuracy, trust, and scalability. The article provides a practical blueprint for implementation, including architecture, decision rules, and evaluation strategies.

This article clarifies the relationship between context engineering and prompt engineering, emphasizing that context engineering is a crucial support system for prompt contracts, not a replacement. It details how to build robust context pipelines that deliver governed, admissible evidence to prompts, ensuring accuracy, trust, and scalability in LLM applications. Learn how to implement contracts first, then build context pipelines.

Don't fall for the "context engineering" rebrand! This article argues that effective prompt engineering, when implemented with clear contracts and policies, already encompasses and controls the entire context lifecycle. Learn how to avoid common pitfalls like drift, privacy creep, and cost bloat by prioritizing prompt contracts over simply adding more context. Discover the proper stack and a concrete example of renewal rescue.

Unlock the power of AI by prioritizing prompt engineering before context engineering. Learn how prompts define the rules, roles, and boundaries for AI models, ensuring reliable, auditable, and actionable results. Discover practical templates and implementation steps to avoid common pitfalls like data drift and privacy leaks. Master the prompts-first approach for cheaper, safer, and more effective AI systems.

Unlock the power of AI by prioritizing prompt engineering before context engineering. This article reveals how prompts act as the operating contract, guiding AI models to use context effectively, govern decisions, ensure reliability, and transform data into actionable insights. Learn to avoid common pitfalls like privacy creep and cost sprawl by adopting a prompts-first approach for cheaper, safer, and more effective AI systems.

This article argues that prompt engineering, not context engineering, should be the primary framework for building AI systems. It positions context engineering as a subset within prompt engineering, emphasizing the importance of contracts that define model behavior, data admissibility, and output schemas. By prioritizing prompt engineering, organizations can achieve greater reproducibility, policy safety, and cost efficiency, moving AI from demos to dependable production systems. It avoids drift, privacy issues, and inconsistent outputs.

This article argues that context engineering, while valuable, is a subset of prompt engineering, not a superset. Prompt engineering provides the necessary structure and governance for AI systems, ensuring reproducibility, policy compliance, and cost control. Without it, context-first approaches lead to data drift, inconsistent outputs, and unpredictable costs. Treat prompt engineering as the operating system and context as a governed module for reliable AI.

Differentiate prompt engineering from "context engineering" in AI. The article argues that prompt engineering, with its focus on specifications, contracts, and testing, qualifies as true engineering. "Context engineering," often just accumulating data, lacks these crucial elements. Learn how to build robust AI systems by treating prompts as operating contracts and context as governed evidence for predictable and reliable outcomes.

Ditch the 'context engineering' myth! This article argues that reliable AI systems require structured 'prompt engineering' – operating contracts that define role, scope, evidence, and output. Learn how contracts prevent privacy leaks, reduce costs, ensure consistency, and drive measurable business value by governing AI behavior, not just feeding it data. Build an evidence fabric, not a memory dump!

Unlock AI's potential with professional prompt engineering. This article argues that effective prompting, not just context, is crucial for reliable, safe, and valuable AI systems. Learn how to treat prompts as versioned contracts, manage context, and ensure consistent outcomes across text, voice, and other modalities. Essential for building dependable AI workflows.

Explore the rise of the AI-centric economy where intelligence becomes a core factor of production. This article maps its architecture, value creation, and near-term outlook. Learn about the components: data fabric, specialized reasoners, agentic orchestration, and governance. Discover how firms can leverage AI for automation, new categories, and competitive advantage by mastering data, evaluation, and trust.