Context is the constraint nobody plans for.
Models keep getting better. Context management determines whether you can use that capability past 10-15 features.
Full post with steering documents, context budgeting, and truncation strategies: https://t.co/rBEpoJKrVn
Your AI's context window is full. And most of what's in it doesn't matter.
I built a spec pipeline for 20+ feature products. By feature 12, the AI was hallucinating component names. By feature 18, prompts hit 100KB.
More context made it worse. Here's what actually worked. ๐งต
Don't use LLM summarization to compress context. Use rule-based extraction.
I lost a full day when the summarizer dropped a protocol definition it deemed "redundant." Three downstream features invented incompatible versions.
Deterministic extraction: zero cost, microseconds, same output every time.
30 min writing a precise spec saves 3+ hours of rework. 6:1 return.
A team shipping 10 features/quarter saves 25-30 hours by investing 5 hours in better specs.
Stop tuning prompts. Start writing specifications.
Full post: https://t.co/elmY3ZOGPv
Your AI coding agent isn't broken. Your specifications are.
After 18 months of building with AI agents daily, I've learned that "AI drift" is almost always a product problem, not an engineering problem.
Here's a framework for diagnosing where it actually comes from. ๐งต
When your agent builds the wrong thing:
1. Check the spec (does it exist? does it have acceptance criteria?)
2. Check the product artifacts (does it trace to a customer problem?)
3. Check the design (does architecture match requirements?)
4. Only then blame the model
70% of "the agent got it wrong" traces to step 1 or 2.
Specifications are no longer documentation for humans. They're contracts for machines.
The spec is the API contract between product thinking and code generation. Treat it like one.
https://t.co/wVTMPWdL7b
#AI#SDLC#SpecDrivenDevelopment#SoftwareEngineering
Amazon's Working Backwards isn't just product methodology. It's a specification pipeline.
7 steps. 4 human gates. Full traceability
from code to customer evidence.
https://t.co/Hh1Cb9NFgZ
#AI#SDLC#WorkingBackwards#ProductManagement
Amazon's Working Backwards SDLC isn't just for Amazon. A 5-person team that builds the wrong thing burns 25% of runway. Here's how to adopt it in 5-10 days.
https://t.co/cLO8cY5gVQ
3/ The article covers:
โ Three infrastructure patterns that work at scale
โ Why $5K becomes $57K per model in production
โ GPU optimization (50-70% cost reduction)
โ Real decision frameworks for hybrid/cloud/on-prem
๐ Full guide: https://t.co/FgnvWGkd3M
๐ฅ Video: https://t.co/bF8fwQrzmu
2/ Example: Healthcare AI
Month 3: POC approved, $5K/month, ready to deploy
Month 6: Legal says "HIPAA compliance required"
Cost: $5K โ $200K/month
Timeline: 3 months โ 18 months to production
This is exactly how projects end up in the 87%