Josh said:
What are some parts of LLM systems that are currently being hard/hand-coded but that could potentially be placed under the control of generative models themselves, potentially accelerating the increase in LLM intelligence?
ChatGPT said:
This is a very insightful question. Large Language Models (LLMs) are still embedded in a broader architecture that relies on many hand-coded or human-designed components—sometimes for safety, efficiency, or interpretability reasons. However, several of these components could plausibly be made learnable or placed under the control of generative models, potentially accelerating intelligence gains. Here's a breakdown:
1. Prompt Engineering and Tool Use Interfaces
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Currently: Humans or external logic define how LLMs interact with tools (e.g., calling a calculator, searching the web, using plugins).
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Could Be Generative: Models could learn when and how to invoke external tools, how to format the inputs/outputs, and even how to build tools for themselves. Autonomously learning APIs is a likely next step.
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Impact: Could reduce reliance on humans to design interfaces, improving autonomy and generality.
2. System Instructions / Meta-Prompting
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Currently: "System prompts" define behavior—e.g., persona, verbosity, tone—crafted by humans.
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Could Be Generative: A meta-model could generate its own system prompts or fine-tune them over time depending on user interactions and tasks.
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Impact: The model learns how to best guide itself for each task or user, reducing reliance on manual steering.
3. Memory Management and Retrieval Augmentation
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Currently: Vector stores and retrieval logic are human-engineered: what to store, how to retrieve, how to summarize.
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Could Be Generative: Models could learn to create and prune their own memories, decide what’s important to remember, or evolve long-term knowledge storage dynamically.
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Impact: Enables the model to become more agentic and context-aware over long-term interactions.
4. Action Planning / Reasoning Strategies
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Currently: Chains of Thought, Tree of Thoughts, or ReAct are externally applied reasoning frameworks.
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Could Be Generative: The model could autonomously generate and test different reasoning/planning strategies (e.g., recursive self-prompting, simulated experts, etc.) depending on the task.
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Impact: A leap toward meta-cognition and adaptable intelligence.
5. Self-Evaluation and Feedback
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Currently: Humans evaluate model outputs or use reinforcement learning with human feedback (RLHF).
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Could Be Generative: The model generates critiques of its own outputs, simulates a human critic, or engages in debate with copies of itself to improve performance.
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Impact: Reduces training cost and allows rapid bootstrapping of quality control and improvement.
6. Training Curriculum and Data Selection
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Currently: Data is curated and ordered by engineers or heuristics.
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Could Be Generative: The model could choose which data to learn from, which examples to revisit, or generate synthetic data tailored to what it needs to improve.
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Impact: More efficient and targeted learning.
7. Fine-Tuning Objectives and Reward Models
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Currently: Loss functions, reward shaping, and evaluation metrics are human-specified.
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Could Be Generative: A meta-model could design or adapt these based on evolving goals or user feedback.
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Impact: Flexibility in optimizing behavior for complex, changing environments.
8. Architectural Design Choices
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Currently: Architecture (e.g., transformer layers, routing, attention mechanisms) is designed by researchers.
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Could Be Generative: Use of neural architecture search or models that suggest their own improved configurations.
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Impact: Evolutionary step toward self-improving models.
9. Safety and Alignment Filters
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Currently: Hard-coded classifiers or rule-based filters are used to block toxic or dangerous outputs.
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Could Be Generative: Models could learn their own ethical boundaries through simulated moral reasoning or interaction with learned norms.
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Impact: More nuanced and context-sensitive safety mechanisms.
10. Tool Construction and Code Writing Pipelines
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Currently: Human programmers define scaffolding, coding practices, or toolchains the LLMs use.
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Could Be Generative: LLMs could construct and iteratively refine their own development environments, plugins, or assistant modules.
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Impact: Opens the door to recursive self-improvement.
Summary Table
| Component | Current State | Generative Potential |
|---|---|---|
| Tool use interface | Hand-designed formats | Learned invocation and formatting |
| System instructions | Manually set | Self-prompting |
| Memory/retrieval | Hard-coded heuristics | Learnable memory systems |
| Reasoning strategy | Predefined frameworks | Self-generated planning strategies |
| Evaluation/feedback | Human-in-the-loop | Self-evaluation/simulation |
| Curriculum/data selection | Curated datasets | Self-curated/synthetic data |
| Reward functions | Manually designed | Learned objectives |
| Architecture design | Hand-tuned | Self-architecting systems |
| Safety/alignment filters | Rules and classifiers | Learned moral/ethical reasoning |
| Development toolchains | Human-made pipelines | Autonomous code generation & tools |
Final Thoughts
The trend is clearly toward meta-learning—models that learn how to learn, reason, evaluate, and improve themselves. As generative models take control over more of these traditionally human-coded parts, we may see a nonlinear jump in intelligence, similar to compounding returns in recursive optimization.
Let me know if you'd like concrete examples or papers exploring any of these areas.
