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Found 19 results for "few-shot prompting"

🔮Vibe Coding

Few-Shot Prompting for STEM: Claude vs Gemini Across 128 Experiments

We ran 128 experiments comparing Claude Sonnet 4 and Gemini 2.0 Flash on STEM problems with zero-shot through 5-shot prompting. The results challenge common assumptions: accuracy stayed flat while format consistency and token efficiency improved dramatically.

January 21, 202615 min read

#few-shot prompting#STEM education#prompt engineering

🔮Vibe Coding

Can You Teach an AI How to Think? Testing Elaborate Reasoning Protocols on Claude Code

We gave Claude Code a 365-line protocol teaching it to think like an ICPC World Finalist. After 300 experiments, the elaborate methodology provided zero improvement over a simple "solve this problem" prompt—but the reasons why reveal something important about agentic AI.

January 27, 202610 min read

#vibe-coding#Claude-Code#agentic-AI

🔮Vibe Coding

XML vs Natural Language Prompts: An Experimental Comparison

We tested XML-structured versus natural language prompts across 144 experiments spanning code generation, mathematics, reasoning, and complex multi-step tasks. The results reveal significant efficiency gains—but with important caveats.

January 15, 20269 min read

#vibe-coding#prompt-engineering#XML-prompts

🔮Vibe Coding

Testing LLM Reasoning on Novel Problems: What 400 Experiments Reveal

We ran 400 experiments on competitive programming problems LLMs have never seen before. The results reveal that when memorization is eliminated, prompt formatting matters far less than model selection.

January 16, 202611 min read

#vibe-coding#LLM-benchmarks#novel-problems

🔮Vibe Coding

Vibe Coding: How Stateless LLM Architecture Powers Intent-Driven Development

Discover vibe coding—a revolutionary approach leveraging stateless LLM architecture to turn natural language prompts into code. Learn prompt engineering techniques for scalable, creative development.

January 13, 20266 min read

#vibe-coding#LLM-architecture#stateless-LLM

🔮Vibe Coding

The Flow-State Experiment: Measuring Papercuts in AI Coding Tools

A systematic comparison of Claude Code, Codex CLI, and Gemini CLI measuring the small friction points that disrupt developer flow. Our experiments reveal fundamental differences in how these tools handle mid-task corrections and context.

February 5, 202612 min read

#ai-coding#claude-code#codex-cli

💻Tech Skills

LangChain Output Parsers: Parse LLM Responses (Complete Guide)

Master LangChain output parsers for structuring LLM responses. Learn about StrOutputParser, PydanticOutputParser, and custom parsers.

March 1, 202616 min read

#langchain#llm#ai

🔮Vibe Coding

The Full Papercut Audit: Where AI Coding Tools Break Down

A comprehensive friction analysis across five dimensions: bracket completion, comment verbosity, over-engineering, multi-file projects, and iterative modification. Gemini CLI achieves perfect zero friction.

February 5, 202612 min read

#ai-coding#claude-code#codex-cli

🔮Vibe Coding

Vibe Coding: Master AI-Assisted Programming with LLM Probability

Learn what vibe coding is, how LLM probability powers AI-assisted programming, and master the skills for the future of coding. Practical examples included.

January 6, 20266 min read

#vibe coding#AI programming#LLM

🔮Vibe Coding

Why LLMs Can't Revise Tokens: The Feed-Forward Constraint Every Developer Must Understand

Learn why LLMs hallucinate and can't self-correct. Understand feed-forward token generation and master vibe coding strategies for better AI-assisted development.

January 6, 20269 min read

#LLM#token generation#feed-forward

🔮Vibe Coding

The LLM Collaboration Guide: How to Avoid 20 Critical Bugs in Production

Learn how to turn LLMs from a liability into your most powerful engineering tool. Discover the three-phase workflow, constraint matrix framework, and production readiness checklist that prevents critical bugs in production.

March 12, 202624 min read

#LLM#AI-Assisted Code#Production

🔮Vibe Coding

The Silent Failure Cascade: Why LLM-Powered Code Breaks in Production

LLM-generated code often fails silently in production due to implicit assumptions. Learn why this happens, how to detect it, and proven strategies to write defensive code that survives the real world.

March 7, 202612 min read

#LLM#AI-Assisted Code#Debugging

🔮Vibe Coding

Attention Mechanisms in LLMs: The Complete Guide to How Transformers Really Work

Learn how attention mechanisms power large language models (LLMs) like GPT-4 and Claude. This in-depth guide explains Query-Key-Value math, multi-head attention, and long-context processing with real code examples.

January 6, 202612 min read

#attention mechanisms#transformers#LLM

🔮Vibe Coding

The Idiomatic Code Audit: Do AI Tools Write Modern Code?

Testing whether Claude Code, Codex CLI, and Gemini CLI produce modern, idiomatic, and secure code by default. Our experiments reveal a critical gap in library version awareness.

February 5, 202610 min read

#ai-coding#claude-code#codex-cli

🔮Vibe Coding

The Hidden Cost of "Ship It Fast": When Velocity Becomes Technical Debt

A post-mortem on vibe coding—the practice of prioritizing momentum over maintainability. Learn why shipping fast without foundations is the most expensive shortcut you'll ever take, and how to recover.

January 14, 202612 min read

#vibe-coding#technical-debt#software-engineering

🔮Vibe Coding

Temperature Sensitivity in LLM Code Generation: A 752-Run Experiment

We tested how temperature (0, 0.3, 0.7, 1.0) affects code generation accuracy on 100 competitive programming problems. The surprising result: temperature barely matters for problem-solving, but model selection does.

January 25, 202610 min read

#vibe coding#LLM temperature#Gemini

🔮Vibe Coding

Why LLMs Fail at Complex Tasks: The Math Behind Compounding Error in AI Code Generation

Learn why LLMs fail on long reasoning chains due to compounding error. Includes mathematical proofs, real-world AI coding failures, and actionable engineering strategies to mitigate AI hallucinations in software development.

January 6, 202611 min read

#LLM limitations#compounding error#AI hallucinations

🔮Vibe Coding

Screenshot-to-Code vs. Specs-Driven Development: The 2026 Design Handoff Evolution

A real-world experiment reveals the profound gap between AI visual approximation and production-ready code. Learn why context pipelines are the future of design-to-code workflows.

January 9, 20268 min read

#vibe coding#AI development#design systems

🔮Vibe Coding

The Generative Core: Understanding Hallucination in Large Language Models

Hallucination isn't a bug in LLMs—it's the generative process itself. Understanding this changes how we should think about working with these tools.

January 6, 202610 min read

#LLM hallucination#AI generation#confabulation