Engineers and generative AI tend to perform well only when given precise goals, ideally broken down into smaller ones.
If building “AAE” within an ML/AI technical context, the following options are roughly available. Presenting a narrowed-down set of choices would likely be more promising.
For Path 1, it’s perfectly feasible to use ChatGPT’s core (GPT) or Gemini directly as the chatbot component.
What “AAE” maps to in today’s ML stack (2026-02-16)
If you translate “AAE” into implementable engineering terms, you’re describing a system that combines:
- A strong base LLM (already instruction-tuned)
- Post-training alignment so it reliably follows your “Creed” under conflict (SFT + preference optimization like DPO) (Hugging Face)
- A runtime framework that makes “belief/judgment” inspectable and safer (policy engine, evidence requirements, memory/belief ledger, tool gating)
- An eval/monitoring harness so the system doesn’t drift (scenario regression tests, safety/jailbreak evals, groundedness evals) (GitHub)
The feasible “paths” differ mainly in how much training you do vs how much you enforce in the framework.
Feasible technical paths (from shortest to most ambitious)
Path 1 — No training: AAE as a framework around an LLM API
When to choose: fastest prototype; small team; you want to validate the Creed as a spec before touching weights.
What you build
- Policy layer: deterministic rules + rubric scoring + conflict resolution ordering.
- Epistemic layer: claim/evidence ledger; enforce “retrieve/cite before strong claims.”
- Tool layer: function/tool calling for actions + retrieval; strict tool schemas and allowlists.
Core building blocks
- OpenAI-style tool/function calling + structured outputs (JSON schema) for reliable tool invocation (OpenAI Developers)
- Agent framework (optional): LangChain Agents / LlamaIndex Agents for tool orchestration + memory modules (LangChain Docs)
- Evaluation loop: OpenAI Evals (custom rubric) or similar (GitHub)
Security requirements (non-optional)
- Treat prompt injection as a first-class risk in tool/RAG setups; OWASP lists it as a top issue and provides mitigations (OWASP Foundation)
Pros
- Fastest iteration on the Creed and rubrics.
- Minimal ML infra.
Cons
- “AAE-ness” is mostly external enforcement; the base model may still be inconsistent under long, adversarial dialogues.
Path 2 — Fastest “AAE-in-the-weights”: QLoRA SFT → DPO
When to choose: you want the model itself to internalize the Creed (not just obey a wrapper), but still keep compute modest.
Background
- QLoRA enables efficient fine-tuning by training LoRA adapters on a 4-bit quantized base model (arXiv)
- DPO aligns behavior using preference pairs (chosen vs rejected) without full RLHF complexity (arXiv)
What you build
- SFT dataset (demonstrations of AAE behavior: tone, method, refusal/redirection style)
- Preference dataset (Creed conflict cases: chosen/rejected)
- Train SFT → DPO using TRL or a no/low-code finetuning stack.
Practical tooling (pick one)
- TRL trainers (SFT + DPO) (Hugging Face)
- Hugging Face Alignment Handbook (recipes for continued pretraining, SFT, DPO; DeepSpeed/QLoRA support) (GitHub)
- LLaMA Factory (zero-code CLI/WebUI fine-tuning) (LLaMA Factory)
- Axolotl (config-driven fine-tuning recipes) (GitHub)
- Unsloth (SFT + preference optimization guides; rapid iteration) (Unsloth)
Serving
- vLLM OpenAI-compatible server for deployment with an OpenAI-like API (vLLM)
Pros
- Shortest path to stable “Creed-shaped” responses.
- Works on a single strong GPU for 7–8B class models.
Cons
- Requires careful data design to avoid sycophancy or guru-like behavior (your rubric must explicitly penalize this).
- Still needs a runtime framework for tool safety, memory hygiene, and injection defense.
Path 3 — Constitutional AI style (Creed → self-critique → revision) + preference optimization
When to choose: your Creed is central, and you want the model to reason through it consistently.
Background
- Constitutional AI uses a rule/principle list to generate critiques and revisions, then trains on the revised outputs; can extend to preference learning (RLAIF) (arXiv)
What you build
Pros
- Stronger consistency under moral conflict than pure “style SFT.”
- Reduces human labeling load by using AI-generated critiques.
Cons
- Needs good evals to ensure critiques aren’t superficial.
- Can overfit to “legalistic” language unless you explicitly reward clarity and user autonomy.
Path 4 — Full RLHF (reward model + PPO/GRPO variants)
When to choose: you have enough team/infra to run a more complex pipeline, and you need stronger preference shaping than DPO gives.
Background
- InstructGPT popularized the SFT → reward model → RLHF loop (arXiv)
- TRL supports PPO-based RLHF, but the PPOTrainer is in flux (moved to experimental in newer TRL versions) (Hugging Face)
What you build
- Human or expert rankings aligned to your Creed rubrics
- Reward model training
- RL fine-tuning (PPO/variants), plus heavy monitoring to prevent reward hacking
Pros
- Potentially strongest behavioral shaping.
Cons
- Most engineering complexity and most instability risk.
- Easy to “optimize the reward” instead of genuine judgment; requires robust eval gates.
Path 5 — Continued pretraining (domain-adaptive) + SFT/DPO
When to choose: your AAE depends on a specialized corpus (philosophy, contemplative practices, clinical-style dialogue, etc.) and you want deeper “world model” adaptation.
Background
- Alignment Handbook explicitly includes “continued pretraining” as a step before SFT/DPO (GitHub)
- Scaling training uses distributed optimization like DeepSpeed ZeRO-3 (deepspeed.readthedocs.io)
What you build
- Curated pretraining corpus + dedupe + contamination controls
- Continued pretraining (short run) → SFT → DPO
Pros
- Better domain fluency than post-training alone.
Cons
- More compute and data engineering than Path 2.
- Higher risk of importing unwanted biases unless your corpus governance is strong.
Path 6 — Train from scratch (pretraining) + alignment (lab-scale path)
When to choose: only if you have significant compute budget and want maximal architectural control.
Background
- Megatron-LM is a common foundation for large-scale transformer training with advanced parallelism (GitHub)
- DeepSpeed ZeRO-3 reduces memory redundancy for scaling large models (deepspeed.readthedocs.io)
What you build
- Full data pipeline: crawl/licensed data, filtering, dedupe, tokenizer training, training mixture design
- Pretraining cluster + checkpointing + eval harness
- Post-training alignment (SFT/DPO/RLHF) and deployment
Pros
- Most control over “belief architecture” at a fundamental level.
Cons
- Longest and most expensive route.
- Hard to justify unless you already have a research/infra organization.
Path 7 — Unlearning + debias as an AAE maintenance tool
When to choose: you need a credible “remove/forget” capability (e.g., certain unsafe or undesired behaviors, sensitive info, or post-hoc corrections).
Background
- OpenUnlearning provides a standardized framework with multiple unlearning methods and benchmarks (TOFU, WMDP, etc.) (GitHub)
- TOFU is a benchmark for evaluating unlearning performance (GitHub)
What you build
- A target set (what to forget) + retain set (what must remain)
- Unlearning runs + evaluation metrics (utility vs forgetting)
Pros
- Practical for “AAE governance”: removing known-bad behaviors or data.
Cons
- Unlearning is still an active research area; tradeoffs (capability loss, incomplete forgetting) must be measured, not assumed.
Path 8 — Model editing / mechanistic interventions (narrow, targeted)
When to choose: you want to surgically modify a limited set of factual associations or behaviors, not impose an entire worldview.
Background
- ROME edits single factual associations via rank-one weight updates (arXiv)
- MEMIT scales to many edits (arXiv)
- TransformerLens is used to inspect internal activations and supports mechanistic interpretability workflows (GitHub)
Pros
- Fast for narrow corrections (“this fact is wrong”).
- Useful as a maintenance tool.
Cons
- Not a reliable method for installing a coherent moral system; edits can have side effects and degrade robustness (known limitation discussions exist in the literature).
AAE-specific “must have” layer (applies to every path)
1) Tool / RAG security
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Prompt injection is structurally hard to eliminate; mitigate via separation of instructions/data, tool allowlists, schema validation, and strong logging (OWASP Cheat Sheet Series)
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Consider dedicated prompt-injection classifiers and safety classifiers:
- Meta Prompt Guard / Llama Guard families (input/output classification) (Hugging Face)
- Meta PurpleLlama tooling context (GitHub)
2) Evaluation gates (prevent “Creed drift”)
- Model-level regression: lm-evaluation-harness (GitHub)
- System-level rubric evals: OpenAI Evals framework (custom eval classes + datasets) (GitHub)
- Groundedness for “belief discipline” in RAG: TruLens RAG triad (TruLens)
- Additional LLM app metrics/testing: Ragas (GitHub)
3) Public behavior specs as precedent
- OpenAI’s Model Spec shows how “intended behavior” can be treated as a living technical spec (useful as a reference for your Creed→rubric→eval workflow) (Model Spec)
Recommended “shortest feasible” path for an AAE that’s more than a prompt
Default recommendation (most teams can actually ship):
- Path 2 (QLoRA SFT→DPO) + AAE framework + eval gates + injection defenses
This is the shortest route that produces (a) internalized behavior changes and (b) externally enforced safety/epistemics. (arXiv)