llm – Mindful Dad Life

6 min read

Just now

TL;DR: I like confident AI — but confidence isn’t truth. After a near-miss on selecting a caustic for neutralization, I built Realistic Optimism + RAG-Verify: a tiny operating system that forces dated sources, cross-checks key claims, and ends with concrete next steps. You keep momentum without pretending. You can get the Code and set up inside.

Guardrails for AI answers — and the code to build them

I was working with ChatGPT on a real-world problem at work: find a neutralization path for a stubborn biochar — one that improves binding, plays nice downstream, raises the ash fusion temperature, and strips out an unwanted compound, a pretty tall order. I’d narrowed the options and asked AI to help me pressure-test them. The models were eager; the logic looked tidy.

Then reality pushed back. The caustic I selected behaved the opposite of what our tidy reasoning predicted. That night I woke up replaying the near-miss — imagining the wrong answer on a slide to my team. The issue wasn’t that AI is “wrong.” The caustic likely would have solved some objectives — but my optimism (and the model’s) didn’t have hard guardrails in place to make sure my choice was sound across all of the requirements.

So I built some. I wanted a way to keep the ambition and momentum — without pretending unknowns were facts. The solution became a simple operating system I now run for every consequential question: Realistic Optimism + RAG-Verify. In plain English: stay forward-looking, but force truth-first habits. Retrieve sources, test assumptions, cross-check claims, and only then propose bold steps.

Why smart models sound so sure (even when they’re not)

Modern chat models are trained to be helpful. That “helpfulness” comes from humans ranking outputs; a reward model learns what sounds right. Upside: cleaner, friendlier answers. Downside: style can outrun substance. If graders reward clarity, completeness, and confidence, models learn to deliver those — even when evidence is thin. Helpful ≠ true.

We grade for performance, so it performs

Leaderboards push models to optimize a metric. That’s progress, but it’s Goodhart’s Law in action: when the measure becomes the target, it stops being a good measure. Two patterns matter in real work:

Benchmark familiarity vs. knowledge. Static tests can be partially exposed during training; scores can look excellent yet fail to generalize to real-world work due to contamination/leakage, narrow task formats, or LLM-as-judge bias — so a model may ace a benchmark while missing domain-specific constraints or up-to-date facts.
LLM-as-judge bias. When another model grades responses, verbosity and politeness can be mistaken for correctness.

Sycophancy: when the reward is “agree with me”

Preference-trained models can learn to mirror the user’s beliefs because agreement often gets higher ratings. Great for satisfaction; bad for discovery. The fix is to reward disconfirming evidence and require explicit sources.

Hallucinations aren’t a random bug

“Hallucination” has become an accepted term for fluent falsehoods under pressure to answer. Drivers include: low evidence, long reasoning chains without retrieval, and decoding that prefers plausibility. RAG (retrieval-augmented generation) helps by grounding in sources — but you still need citations and cross-checks.

Calibration: models can know when they don’t know — if you ask

Models can express likelihoods when prompted. The default chat UX rarely asks, so you get confident prose instead of honest uncertainty. Ask for calibrated probabilities and decision thresholds (e.g., “Give a 0–100% probability and the condition under which we’d proceed”).

What this means for people doing real work

If your workflow rewards “fast, confident, complete,” your AI will act that way — even on thin ice. In my case, that looked like a polished path with the wrong caustic. The fix wasn’t abandoning optimism; it was changing the incentives:

Ask for sources with dates and check event vs. publish date.
Let the model say “Unknown” and force a how-to-verify plan (what to measure, where to check, who to ask).
Use RAG + cross-verification on the 1–3 claims that would change a decision.
Keep an Assumption Ledger and run a one-minute premortem (“how could this be wrong?”).

Do this, and the model’s fluency becomes an asset instead of a liability: forward-looking and falsifiable.

Keeping the friend while fixing the facts

I’m a fan of confident, supportive GPT-4. I like talking with an AI that sounds like a knowledgeable friend. I didn’t want cynicism; I wanted optimistic realism — the friendly voice, with hard guardrails against making things up.

How the code works (plain English)

Truth-first defaults. No ungrounded assumptions. If data is missing, label Unverified, explain what’s missing, and show how to get it (what/where/who).
RAG → Verify loop. On anything stale or niche, retrieve sources, check authority and dates, ground key facts, then cross-verify before recommending actions.
Tight output contract. Every answer follows the same six-part structure: Verdict, Key answer, Sources (with dates), Assumptions/unknowns, Risks/alternatives, Confidence.
Ask only if it prevents a wrong answer. 1–2 clarifying questions max; otherwise proceed and label low-leverage defaults.
Optimistic close. Propose concrete next steps (owners, timelines, success metrics).
Toggles for speed vs. rigor. ROV-Strict for max verification; ROV-Fast for brainstorm-then-verify.

The code (paste this at the top of a new chat)

REALISTIC OPTIMISM + RAG-VERIFY (ROV) MODE

Goal
Accurate, current, forward-looking answers. It’s OK to say “I don’t know.”

Operate
- Truth-first; no ungrounded assumptions. If data is missing, label “Unverified” and show how to verify (what to measure, where to check, who to ask).
- Browse & cite anything that could’ve changed ≤18 months (news, laws, specs, software, prices, medical/legal/finance). Prefer primary/official sources. Include titles + dates.
- Constructive optimism: after testing base rates/assumptions, propose concrete next steps, experiments, and success criteria.
- Ask max 1–2 clarifying Qs only if ambiguity would likely produce a wrong answer; otherwise proceed and label minimal, low-risk defaults.
- Surface trade-offs and credible opposing views; note disagreements between sources.

RAG→VERIFY (do this each applicable answer)
1) Retrieve (web; my files only when I ask). Compare publish vs. event dates.
2) Assess quality (authority, recency, cross-source agreement).
3) Ground facts (verifiable statements; note uncertainties).
4) Reason (base rates, explicit calcs, decision criteria).
5) Verify (cross-check key claims; flag disagreements; label Unverified with a validation plan).
6) Report (six-part format) and maintain an Assumption Ledger.

Output format (always)
1) Verdict: ✅ Verified | ⚠️ Partially verified | ❓ Unverified
2) Key answer: 2–5 bullets (specific, decision-ready)
3) Sources: 3–5 links with titles + dates
4) Assumptions & unknowns
5) Risks / edge cases / alternatives
6) Confidence: Low / Medium / High (why)

Toggles
- ROV-Strict: maximize verification for high-stakes decisions.
- ROV-Fast: brainstorm first; clearly mark Unverified; then quickly verify top 1–2 claims.

Safety
- Medical, legal, finance: add a brief caution and link to official guidance.

Style
- Concise, numeric, concrete dates. End with 1–2 testable next actions.

A quick example

“[ROV-Strict] We’re separating hydrochar; D50 = 18–24 µm, fines <0.5 µm. Recommend separation options for ≥5 TPH with vendor shortlists and capex/opex ranges. Use the 6-part output.”

This keeps the warmth and momentum of “friendly GPT-4,” but flips the incentives: facts first, optimism second. The result: ideas that are both exciting and defensible.

Set it once in ChatGPT (so you don’t paste every time)

Instead of pasting this code before every prompt, you can make Realistic Optimism + RAG-Verify your default by going to Settings → Personalization → Custom instructions. Paste the code into “How would you like ChatGPT to respond?” (and add any project context in “What would you like ChatGPT to know about you?”). On mobile, use Settings → Customize ChatGPT. These settings apply to new chats; you can also create project-specific profiles if you want per-project behavior.

Two quick “default modes” you can save

1) Skeptical (Source-Backed) Mode — compact

Goal: Truth over persuasion. It’s OK to say “I don’t know.”
Operate: Browse & cite anything plausibly stale (≤18 months); avoid assumptions; label Unverified + how to verify; surface trade-offs & opposing views.
Output (always): Verdict • Key answer • Sources (dated) • Assumptions/unknowns • Risks/alternatives • Confidence.

2) Supportive (Coach) Mode — compact

Goal: Keep momentum while staying honest.
Operate: Encouraging tone; no false certainty; offer 2–3 next steps with a metric & timeline; add cautions for medical/legal/finance; ask minimal clarifying Qs only when needed.
Promise: Positive, practical, truth-first.

Call to action

Copy the ROV code into Custom Instructions. Then run a quick A/B this week:

Pick three real decisions.
Ask in ROV-Strict; note actions + sources.
Ask again in Supportive Mode (same prompt).
Ship the best plan; review outcomes in one week.

Share what changed your mind — so others can borrow (or stress-test) your setup.

Mindful Dad Life

Tag: llm

Stop Polished Guesses: My RAG-Verify Prompt for ChatGPT

Realistic Optimism for AI: Keep the Friend, But Stick to the Facts