From Vibe Coding to Context Engineering
How AI Development is growing up
Post methodology
@notebooklm write an essay on the subject “Does Vibe Coding Need Context Engineering? or “Evolution of Vibe Code in 2025”;
proposed structure: The promise of vibe coding; The evolution from prompt engineering to context engineering; Comparison of context engineering with the OpenAI spec and Claude Code approaches; description of how context engineering can fulfill the promise of prompt engineering;
Talk about the evolution of VibeCoding through 3 phases in 2025
Lean heavily into patterns and anti-patterns
Lean heavily into lessons from the Ulimate Vibe Coding guide
Make this essay about 4000-5000 words
@claude: read the text of the essay in the prompt and your key insights (generated earlier) and write a 2000 word post that summarizes the main points in the format for substack post. Please make the post as concise as possible and avoid academic language or footnotes.
@chatgpt read the text of the essay in the prompt and output from claude and write a 2000 word post that summarizes the main points in the format for substack post. Please make the post as concise as possible and avoid academic language or footnotesSummary:
The way we build software with AI is evolving. What began as casual “vibe coding” — chatting with an AI to generate code—is maturing into a structured, scalable practice called context engineering. This post traces the three major waves of this evolution, breaks down the four pillars of context engineering, and shows why managing context—not prompts—is the future of AI-assisted development.
🌀 The Rise (and Limits) of Vibe Coding
In late 2024, a new generation of developers discovered a magical new workflow: vibe coding.
The pitch was seductive:
“Just tell the AI what you want — and it’ll build it.”
No syntax. No boilerplate. No need to wire up config files. You were speaking in ideas. The AI did the rest.
But as projects got more complex, the cracks showed:
🧠 Amnesia: AI forgot architectural decisions between sessions.
🔁 Repetition: You kept re-explaining project goals.
🧩 Incoherence: Outputs didn’t fit together.
👻 Hallucinations: The AI invented features or APIs that didn’t exist.
One dev nailed it:
“It’s like managing a brilliant but confused intern.”
You could get snippets, but not systems. You could vibe, but you couldn’t scale. The magic prompt fallacy — that one perfect message would produce a working app — had met reality.
And the root cause was clear: missing context.
🪜 The Three Waves of AI Coding
As 2025 unfolded, developers adapted. The evolution came in three distinct waves:
Wave 1: Discovery
Late 2024 – Early 2025
“Why won’t this AI understand me?”
AI was a novelty — a smarter Stack Overflow. Devs used it for:
Debugging
Snippets
Small one-off tasks
Then came a breakthrough: role-playing.
Give the AI a persona like “Senior Engineer” or “Product Manager,” and it behaved better.
This trick planted a deeper seed:
AI needs structure. It needs role. It needs environment.
But the workflow was still ad hoc, and prompts were ephemeral.
Wave 2: Structure
March – May 2025
“The AI is a junior dev — give it guidance.”
The metaphor shifted. You didn’t just talk at the AI. You treated it like an intern.
The vibe changed from “magic prompt” to “guided collaboration.”
Key practices emerged:
✅ Plan first: Write a vision, strategy, and spec before touching code.
🧾 Use .rules files: Codify stack decisions and naming conventions.
🧩 Break it down: Decompose into 15–30 min atomic tasks.
🔁 Progressive enhancement: Build a skeleton, then layer functionality.
This added discipline. But devs were still manually juggling context across chat sessions, IDE tabs, and memory.
Wave 3: Engineering
Late May 2025 – Present
“We’re not just prompting AI — we’re designing systems.”
This is where things got serious.
We moved from prompting agents to architecting agent workflows.
Instead of throwing prompts into the void, developers started creating infrastructure — persistent, queryable, AI-readable context that scaled across:
Sessions
Models
Tasks
This is the era of Context Engineering.
🧠 What is Context Engineering?
Context engineering is the practice of designing, managing, and delivering the right information to AI systems at the right time — across entire projects, not just prompts.
Just like version control or deployment pipelines, context becomes infrastructure. It’s a system — not a sentence.
This shift mirrors other historical transitions in tech:
HTML → CSS → Component systems
Scripts → Build pipelines → CI/CD
Vibe prompts → Spec-first workflows → Context engineering
🧱 The Four Pillars of Context Engineering
Context engineering stands on four core pillars. These are the patterns powering today’s most productive AI workflows:
1. Context Persistence
AI needs memory.
📝 Session Notes: progress.md or runtime state files to track thinking within a task.
🧠 Project Memory: Long-term documents like architecture.md, ai_context.md, and tech_stack.md that evolve with the project.
🔁 Living Docs: Update after each major feature or bug. Don’t throw away your thinking—codify it.
Think of this as the AI’s second brain.
2. Context Selection
Only load what’s relevant.
🔍 RAG (Retrieval-Augmented Generation): Use embedding search or rule-based fetchers to pull in the right files or specs.
🧭 Hierarchical Docs: Organize docs from general (project root) to specific (module-level).
🧪 Pattern Examples: Give the AI great samples from your codebase so it learns style, architecture, and best practices.
Don’t dump the whole repo. Curate its view like a good teacher would.
3. Context Compression
Keep only what matters.
🧾 Summarization: Condense past work, chat history, or long tool outputs into distilled forms.
✂️ Trimming: Use heuristics to prune stale or irrelevant messages.
🤖 Auto-Compaction: Some tools like Claude Code do this for you near context limits.
Prevent “context poisoning” with lean, meaningful sessions.
4. Context Routing
Different agents need different context.
🎭 Role-Based Agents: Architect, Coder, Reviewer — each gets different slices of context.
📦 Formal Handoffs: handover.md files summarize decisions, recent changes, and next steps.
🔁 State Anchoring: Use Git SHAs to lock AIs to specific points in the codebase, like “context bookmarks.”
This is where agent-based development gets orchestration.
🔧 Spec Coding: Intent as Infrastructure
One of the most elegant outcomes of context engineering is Spec Coding — championed by the team at SpecStory and inspired by Claude’s emerging workflows.
The idea:
Intent is the new source code.
You don’t start with implementation.
You start with clarity:
What are we building?
Why does it matter?
Who’s it for?
What are the constraints?
The flow looks like this:
Vision & Strategy — product goals, user personas, metrics
PRD — user stories, features, UX flows, requirements
Tech Spec — architecture, design system, content outline
Task Breakdown — development, design, and QA tasks
Execution with AI — each step validated by human and agent collaborators
This isn’t just good process. It’s how AI performs best.
⚡ Why This Evolution Matters
✅ Developers become Architects
You don’t micromanage code — you guide systems. The human role shifts from implementer to orchestrator.
🧠 Knowledge Compounds
Instead of losing context every chat, reasoning is preserved. Every session makes the AI smarter.
🚀 Onboarding Accelerates
Whether it’s a new human or a new agent, well-maintained context speeds things up dramatically.
🧪 Quality Becomes Predictable
You’re not gambling with prompts. You’re engineering conditions that reliably produce great code.
🧭 How to Make the Shift
Still prompting casually? No shame. But if you want leverage, start shifting toward context engineering.
Start Simple:
Create /ai/docs/ in your repo
Add a vision.md, architecture.md, and ai_context.md
Track task progress in progress.md
Break requests into 15–30 min chunks
Scale Up:
Orchestrate multi-agent workflows (Planner, Coder, Reviewer)
Use Git SHAs in memory files for time-anchored context
Set up RAG pipelines to fetch relevant docs automatically
Build handoff protocols between sessions
🌱 From Demo to Discipline
Vibe coding was fun. It was experimental. It was how we all started.
But the future belongs to those who treat AI collaboration as infrastructure — who move from hoping the AI “gets it” to ensuring it does.
We’re not replacing code.
We’re elevating how it’s built.
Welcome to the age of Context Engineering.
Sources:
YouTube Video: “Context Engineering for Agents” - LangChain
GitHub Repository: “The Ultimate Vibe Coding Guide” by Nicolas Zullo (EnzeD/vibe-coding)
Specflow Article: “Patterns & Best Practices” - Specflow
Specflow Article: “Reddit Wisdom” - Specflow
YouTube Video: “The New Code — Sean Grove, OpenAI” - AI Engineer