I’ve tested countless models, and while the tech is amazing, it’s far from easy. Most people fail because they treat prompts like magic spells. Once you understand these 5 rules, the “brain fog” disappears:
1. Stop Over-Prompting (The “Less is More” Rule) Long, complex prompts often cause “attention drift.” The AI starts overthinking the instructions and forgets the goal. Instead of one giant prompt, use a clear structure and give the model one task at a time.
2. Never Drop Below Q4 Quantization Using Q2 or Q3 models is the fastest way to get hallucinations. These “thin” models lack the weights to hold complex logic. Use Q4_K_M or higher—it’s the “sweet spot” where the AI stays grounded and reliable.
3. Provide “Grounding” via API (e.g., Brave Search) An AI without a data source is just a “hallucination machine.” Connect it to a search API with a strict token limit. Real-time data keeps the agent honest and prevents it from making things up when it doesn’t know the answer.
4. Filter for Stability, Not Hype Don’t chase every new “benchmark king” on Hugging Face. Check the download counts and user feedback. A stable, older model is always better for an agent than a flashy new one that crashes under pressure.
5. Research the Model’s “Ceiling” Every model has a limit. Find out what it can’t do before you start. For example, never give an AI unsupervised access to your system files—always set boundaries and keep a “human in the loop” to verify its actions.
Great list — and I agree with every point as practical advice. But I think it’s worth zooming out, because all five rules are essentially compensations for a missing layer.
“Stop Over-Prompting” — Exactly. But why do we over-prompt? Because we’re encoding logic, control flow, and context management inside the prompt itself. In ORCA, reasoning is decomposed into skills — small, declarative, reusable units. The prompt stays minimal because the structure carries the intent, not the text.
“Never Drop Below Q4” — True for unstructured generation. But when you externalize reasoning into a cognitive runtime, the model’s job shrinks: it executes one well-scoped step at a time, not an entire chain of thought. That changes the quantization equation — structured execution is more forgiving on model capacity.
“Provide Grounding via API” — 100%. In ORCA this is formalized through bindings — typed connectors between skills and real services (APIs, search, databases). Grounding isn’t an afterthought; it’s a first-class architectural element.
“Filter for Stability” — Agreed, but with structured skills you gain something bigger: model portability. Your agent logic lives in capabilities, not in a model-specific prompt. Swap the model without rebuilding the agent.
“Research the Model’s Ceiling” — This is where capability contracts come in. Each ORCA skill declares its inputs, outputs, and boundaries before execution. The ceiling is explicit and enforceable, not discovered through trial and error.
Your rules are solid engineering discipline. What I’m exploring with ORCA is whether we can encode that discipline into the runtime itself — so it’s not advice developers need to remember, but structure the system enforces.