Why AI Coding Still Fails — and How Context Graphs Could Fix It

#ai #coding #programming #webdev

Large Language Models (LLMs) are amazing at writing code — until you start talking to them like a human.
You describe a feature, they code something. Then you say “actually, make it multi-user,” and the logic collapses. Why does this happen?

A new research paper just revealed the hidden reason — and it perfectly explains why Crevo was built the way it is. To let more people experience Crevo, share a 50% discount code 3EUSTLMI, valid for 30 days.

I. Why Does AI Programming Often Go Off the Rails?

In the world of AI-assisted programming, users rarely articulate all their requirements perfectly in one go. Instead, they clarify their needs through an iterative, multi-turn conversation.

However, recent research shows that the success rate of Large Language Models (LLMs) drops significantly in multi-turn, underspecified conversations.
This presents a serious challenge for AI programming tools — especially those built on a conversational development model.

II. The Research: Full Context Upfront Boosts Accuracy by 35%+

In a May 2025 paper titled “LLMs Get Lost in Multi-Turn Conversation” (arXiv:2505.06120), researchers tested over 200,000 simulated dialogues across 15 mainstream models, comparing single-turn vs. multi-turn performance on six generation tasks (including code generation and database queries).

Prompting Method	Description	Average Success Rate
One-Shot Prompting	Providing all requirements at once.	78%
Multi-Turn Refinement	Clarifying requirements step-by-step.	54%

They found that in multi-turn setups, LLM performance drops by 39% on average — mainly because of two issues:

Aptitude Decline: Even at its best, multi-turn performance lags ~16% behind one-shot setups.
Reliability Collapse: Error rates skyrocket due to inconsistent internal reasoning (up by ~112%).

“When an LLM receives the full context in the first prompt — including features, constraints, and interfaces — the correctness and structural integrity of the generated code improve dramatically.”

In short: LLMs don’t handle partial context well. They perform best when given the whole picture upfront.

III. What This Means for AI Programming

Developers often rely on “progressive clarification”:

Tell AI the goal.
Let it code something.
Then say “add this” or “change that.”

But if the model misunderstood something early on, subsequent corrections can’t fix the foundation.
Yet cramming all requirements into one giant prompt is impractical — too long, too costly, and too complex.

AI programming tools therefore need a balance between iterative convenience and contextual completeness.

This is where Crevo’s design comes in.

IV. Crevo’s Solution: Full Context, On-Demand Retrieval, and Iteration-Driven Prompts

1. The Core Idea: Layered Docs + Context Retrieval

Crevo organizes the entire development lifecycle into structured, retrievable layers:

Dimension	Content
User Stories	Goals and scenarios
PRD	Features, logic
System Architecture	Modules and dependencies
Database Design	Tables and constraints
API Specs	Input/output contracts
UX/UI	Mockups and flows
Dev Plan	Iteration priorities

When you begin a task like “implement user login”, Crevo automatically retrieves all related fragments — stories, DB schemas, APIs, UI, etc.
It then composes them into a complete, unified context for the model.

Even though you work iteratively, the model operates in a quasi-single-turn environment — always with full context.

2. Why This Works

Crevo’s approach aligns perfectly with the paper’s recommended “CONCAT strategy” — combining fragmented turns into one rich prompt.

This method ensures:

Precision + Completeness: Retrieves only relevant details, but all of them.
Stable Context: The model never “forgets” or misinterprets earlier data.
Error Recovery: New prompts reconstruct full context to avoid cascading errors.
Developer Efficiency: No more repeating background info.

3. In Practice

You say: “Build a user login endpoint.”
Crevo retrieves the login flow, DB schema, API spec, and UI mockup.
It builds a full prompt → generates backend code, tests, and docs.
You add: “Include captcha and ‘remember me.’”
Crevo reassembles the updated context and regenerates the complete version.

V. From Prompts to a Context Graph

Crevo’s philosophy isn’t “write better prompts” — it’s build better context.

By structuring all project information as a context graph, Crevo enables true project-level memory and global reasoning for AI.
The future of AI programming isn’t about crafting smarter prompts — it’s about engineering smarter context.