Large language models like GPT-4, Claude, and Gemini have transformed software development through AI-assisted coding. Yet beneath the impressive outputs lies a fundamental constraint that every developer should understand: once an LLM commits to a token, it cannot revise that decision.
This isn't a bug—it's the architecture. LLMs use a feed-forward neural network design where each token prediction flows in one direction only. Think of it as writing with permanent ink on a continuously unrolling scroll. The model can only add to what exists; it cannot erase or modify previous words.
This constraint directly explains why LLMs hallucinate, why errors compound rapidly, and why vibe coding requires human oversight to produce reliable results.
How LLM Token Generation Actually Works
Understanding the token generation process reveals why self-correction is architecturally impossible during inference.
The Four-Step Token Generation Loop
- Process the current sequence — The model reads the entire prompt plus all previously generated tokens
- Calculate probability distribution — Based on this context, the model computes the likelihood of every possible next token
- Sample one token — A single token is selected (deterministically or with randomness based on temperature)
- Append and repeat — The chosen token joins the permanent sequence, and the process restarts
Each step moves forward. There is no backward pass during generation, no revision mechanism, no "undo" capability.
Why This Creates Local Optimization Without Global Correction
The feed-forward design optimizes for immediate coherence—each token makes sense given what came before. But the model has no ability to evaluate whether the overall direction is correct.
Consider this analogy: you're navigating a maze where you can only see the immediate path ahead. Each step might look reasonable, but without the ability to backtrack, a single wrong turn locks you into an increasingly problematic route.
The Self-Correction Illusion: Why "Reflection" Isn't What It Seems
When users observe an LLM apparently "catching its mistake," they're witnessing something fundamentally different from genuine self-correction.
What Actually Happens During Apparent Self-Correction
def generate_llm_output(prompt, steps):
sequence = prompt
for _ in range(steps):
# Model treats ALL previous tokens as ground truth
next_token = model.predict(sequence)
sequence += next_token
return sequence
When an LLM writes "Wait, I made an error above," it isn't revising anything. It's adding new tokens that acknowledge previous tokens. The original mistake remains embedded in the sequence and continues influencing all subsequent generation.
This distinction matters enormously for practical applications. The model responds to its own output as fixed history, not as malleable draft text that can be refined.
Why LLM Errors Snowball: The Hallucination Cascade Effect
The feed-forward constraint creates a specific failure mode: error amplification through forced consistency.
How a Single Wrong Token Corrupts Everything Downstream
Once the model commits to an incorrect assumption, architectural constraints force it to maintain consistency with that error. The result is increasingly elaborate justifications for an initially flawed premise.
Example: Hallucinated Code Dependencies
def parse_response(data):
import json
import requests # Unnecessary import—early commitment to wrong approach
response = requests.Response() # Fabricating objects to justify the import
response._content = data
return json.loads(response.text) # Building on the fabrication
The model didn't intend to hallucinate. It made an early decision (importing requests) and then worked to maintain logical consistency with that choice, even though simpler solutions existed.
Why LLMs Optimize for Coherence Over Accuracy
The training objective rewards plausible-sounding text. When forced to choose between internal consistency and factual correctness, the architecture biases toward consistency—the model cannot step back and reconsider its approach.
Vibe Coding and Feed-Forward Constraints: What Developers Need to Know
Vibe coding—the practice of describing desired functionality in natural language and letting AI generate the implementation—amplifies both the benefits and risks of feed-forward architecture.
Why Early Tokens Matter Most in AI-Generated Code
The first 5-10 tokens often determine the entire trajectory of generated code. Import statements, function signatures, and initial algorithm choices create constraints that propagate throughout the output.
Three Critical Vibe Coding Strategies
-
Monitor trajectory early — Watch the initial output carefully; stopping and regenerating is cheaper than debugging deep structural problems
-
Use skeleton-first prompting — Lock in correct high-level structure before requesting implementation details
-
Implement checkpoint validation — Explicitly confirm intermediate results before allowing the model to continue
How to Work With Feed-Forward Constraints: Practical Techniques
Since LLMs cannot self-correct during generation, effective AI-assisted development requires external feedback loops.
Before and After: Human-Guided Code Improvement
Initial flawed generation:
def calculate_average(nums):
sum = 0 # Shadows built-in
for n in nums:
sum += n
avarage = sum / len(nums) # Typo compounds the problem
return avarage
Corrected after human feedback:
def calculate_average(nums):
total = 0
for n in nums:
total += n
average = total / len(nums)
return average
The correction happens between generation cycles—not within them. This is the fundamental pattern for effective LLM collaboration.
Temperature Settings for Different Development Phases
| Development Phase | Recommended Temperature | Rationale |
|---|---|---|
| Architecture decisions | 0.0 – 0.2 | Minimize variance in foundational choices |
| Algorithm implementation | 0.3 – 0.5 | Balance creativity with reliability |
| Creative problem-solving | 0.7 – 0.9 | Explore diverse approaches |
| Final production code | 0.0 | Ensure deterministic, reproducible output |
Five Advanced Prompting Strategies
1. Skeleton-First Generation
# Request this first:
def authenticate_user(username: str, password: str) -> bool:
"""Authenticate user against database. Return True if valid."""
pass
# Then request implementation separately
2. Constrained Context Windows Ask for one function at a time rather than entire systems. Smaller scope means fewer opportunities for error cascades.
3. Explicit Checkpoints Include phrases like "The schema above is correct. Now implement the query function." This creates mental anchors for the model.
4. Regeneration Over Continuation When you spot an error, restart generation rather than asking for fixes within the same context.
5. Separation of Concerns Generate tests separately from implementation. Cross-validate outputs against each other.
Critical Decision Points: Where Token Commitment Matters Most
Certain early decisions have outsized impact on code quality. Watch these carefully:
- Import statements — Wrong dependencies propagate through entire files
- Function signatures — Parameters and return types constrain implementation options
- Data structure choices — The initial algorithm approach is difficult to reverse
- Error handling patterns — Early exception strategies affect all downstream logic
- Naming conventions — Variable names influence how the model interprets scope and purpose
The Developer's New Role: From Coder to AI Director
Effective vibe coding requires a mindset shift. You're no longer writing code line by line—you're directing an improvisational performer.
What This Means in Practice
- Set the scene precisely — Clear, specific prompts reduce ambiguous decisions
- Be ready to call "cut" — Recognize when generation has gone off-track and restart
- Edit between takes — Make corrections between generation cycles, not during them
- Review the final product — AI-generated code requires human verification before deployment
Summary: LLM Architecture Strengths and Limitations
| Architectural Component | Strength | Limitation |
|---|---|---|
| Probabilistic generation | Extremely fast output | No guarantee of accuracy |
| Attention mechanism | Strong context awareness | Signal dilution over long contexts |
| Feed-forward processing | Computational efficiency | No revision or self-correction |
| Token-by-token prediction | Fine-grained control | Error lock-in from early mistakes |
Frequently Asked Questions
Why do LLMs hallucinate?
LLMs hallucinate because their feed-forward architecture forces consistency with previous tokens, even when those tokens contain errors. The model optimizes for coherent continuation rather than factual accuracy, leading to confident-sounding but incorrect outputs.
Can LLMs correct their own mistakes?
LLMs cannot revise previously generated tokens during inference. What appears as self-correction is actually the model adding new tokens that acknowledge or work around earlier output—the original errors remain in the context and continue influencing generation.
What is the best way to use vibe coding safely?
Effective vibe coding combines low temperature settings for foundational code, human review at each major checkpoint, and willingness to regenerate rather than patch problematic output. The human developer serves as the external feedback loop that the architecture lacks.
How does feed-forward architecture affect AI coding tools?
Feed-forward constraints mean AI coding tools cannot backtrack or reconsider decisions mid-generation. Early choices about libraries, algorithms, and structure become locked in. Effective use requires careful prompting to guide initial decisions and active monitoring of output trajectory.
Will future LLMs be able to self-correct?
Current transformer architectures are fundamentally feed-forward. Future systems might implement iterative refinement through multi-pass generation or external verification loops, but these would be additions to the generation process rather than changes to the core token prediction mechanism.
Conclusion: Working With LLM Constraints, Not Against Them
The feed-forward constraint isn't a flaw to be fixed—it's a fundamental characteristic to be understood and accommodated. The most effective AI-assisted development workflows embrace this reality through careful prompt engineering, human oversight, and iterative generation cycles.
Understanding why LLMs behave as they do transforms frustration into capability. When you know that models cannot revise their outputs, you naturally adopt patterns that produce better results: precise initial prompts, early intervention when things go wrong, and systematic verification of generated code.
AI-assisted programming isn't replacing traditional development—it's creating a new collaborative paradigm where human judgment and machine speed combine. The developers who thrive will be those who understand both the remarkable capabilities and the architectural constraints of their AI tools.