Prompt Engineering Articles
Browse 175 articles about Prompt Engineering.
How to Use Effort Levels in Claude to Get Better Results Without Overspending
Claude's effort levels—low, medium, high, max—dramatically affect cost and quality. Learn when each level helps and when max effort actually hurts.
What Is the 'Fable Mode' Skill? How to Make Cheaper AI Models Think Like Frontier Models
The Fable Mode skill injects Claude Fable 5's reasoning habits into cheaper models like Opus, using five gates: scope, evidence, attack, verify, report.
What Is Semantic Compression? How to Cut AI Token Costs by 75% Without Losing Quality
Semantic compression rewrites prompts and system files to maximum information density. Learn how to reduce token usage by 75% with zero quality loss.
How to Prompt Claude Fable 5 for Maximum Output Quality: 6 Rules That Actually Work
Claude Fable 5 works best with short prompts, open-ended goals, and rich context. Learn 6 prompting rules from real usage to get the most out of the model.
How to Use Claude Fable 5 Without Triggering the Opus 4.8 Safety Fallback
Claude Fable 5 silently routes certain requests to Opus 4.8. Learn which prompts trigger the fallback and how to avoid it in your agent workflows.
How to Prompt Claude Fable 5 for Maximum Output Quality: 6 Rules from Anthropic
Anthropic's own documentation reveals six prompting rules for Claude Fable 5—including effort levels, negative prompting, and avoiding Opus fallback.
How to Build a Brand Context Folder for AI Agents: Voice Profile, Visual Identity, and Positioning
Stop getting generic AI outputs. Build a brand context folder with voice profile, design tokens, and positioning files that every agent session inherits.
How to Prompt Claude Fable 5 Like an Anthropic Engineer: 6 Rules That Actually Work
Anthropic's own best practices for Claude Fable 5 include giving context, negative prompting, effort levels, and avoiding reasoning requests that trigger Opus.
How to Build a Multi-Perspective AI Research Workflow Using the STORM Method
Stanford's STORM method uses five expert agent personas to produce research 25% more organized than single-prompt approaches. Here's how to build it.
How to Prevent AI Sycophancy in Your Workflows: The Multi-Persona Council Method
AI models agree with you 88% of the time. Learn how to use a multi-persona council—contrarian, buyer, researcher—to stress-test ideas before you build.
How to Prevent AI Sycophancy: Why Your Agent Agrees With Everything and How to Fix It
AI models agree with users 88% of the time. Learn how to use adversarial councils, devil's advocate prompts, and structured critique to get honest AI feedback.
GLM 5.2 Architecture Deep Dive: Index Share, Sparse Attention, and Multi-Token Prediction
GLM 5.2 achieves 2.9x fewer compute operations at 1M token context using Index Share sparse attention. Here's the technical breakdown for AI builders.
Static Context vs Dynamic Context in AI Agents: How to Manage What Your Agent Knows
Static context loads every session; dynamic context loads on demand. Learn how to balance both for token efficiency and reliable AI agent performance.
How to Design Agent Loops with Verifiable Stop Conditions
The best agent loops use objective stop criteria, not subjective ones. Learn how to define done conditions that agents can check deterministically.
Agentic Loop Design: How to Define Goals and Verification Criteria That Actually Work
The quality of an agentic loop depends entirely on its stop condition. Learn how to write objective, verifiable goals that prevent runaway agent sessions.
How to Build an LLM Council: Ensemble AI Agents with Blind Ranking and Synthesis
Learn how to build a multi-model AI council where agents answer independently, rank each other anonymously, and a chairman synthesizes the final answer.
Loop Engineering vs Prompt Engineering: What's the Difference and Which Do You Need?
Loop engineering replaces you as the person who prompts the agent. Learn how it differs from prompt engineering and when each approach makes sense.
Prompt Bloat vs Skill Systems: Why Giant System Prompts Make AI Agents Worse
Stuffing every rule into a system prompt causes agents to lose focus. Learn how modular skill systems solve prompt bloat and reduce the re-explanation tax.
What Is Context Engineering? Why It Matters More Than Prompt Engineering for AI Agents
Context engineering fills the AI context window with the right information. Learn why it outperforms prompt engineering for agentic workflows.
What Is the Slot Machine Method for Claude Code? Why Restarting Beats Correcting
When Claude makes a mistake, arguing makes it worse. The slot machine method says to rewind and re-run instead. Here's why it works and how to do it.