Use this code to stop AI from giving you great sounding guesses and make sure it sticks to the Truth.
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.
- ROV-Off: **disable this mode for the current prompt**—no forced browsing, no six-part output, no verify loop; respond normally while still following safety rules.
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.
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.
We don’t just want artificial intelligence to answer our questions; we want it to know us, to protect what we share in our deepest truths, to help us think. We want AI to be our locked diary and our trusted therapist at the same time. And that desire is shaping one of the most important debates in technology today.
Sam Altman recently admitted that people pour their hearts out to ChatGPT with their most intimate struggles, which convinced him that “AI privilege”, the same protections you’d expect from a doctor or a lawyer, is essential. OpenAI is now moving toward encryption. But encryption, by design, locks conversations away from prying eyes, even from the provider itself. That seems to clash with what people also want: continuity, memory, personalization, and an AI that feels like a true thinking partner.
The Trade-Off Is False
We’re told we must choose: privacy or personalization. But that is a failure of binary imagination, not a law of nature.
Encryption today is treated as all-or-nothing: either the provider can access your data (useful for personalization, dangerous for privacy) or they cannot (safe for privacy, sterile for continuity). But privacy and memory are not opposite ends of one line: they exist on different axes. We’re boxed into this trade-off only because data pipelines were built for advertising-driven tech platforms. The same limited framing shows up in regulation: laws often assume old architectures and reinforce false binaries instead of demanding innovation that makes synthesis possible.
There are better paths:
On-device AI: Running models locally is increasingly possible as hardware improves (Apple and Qualcomm are moving here). This ensures memory and personalization stay with the user. The challenge is resource cost, but techniques like model distillation and edge accelerators make it realistic in the near term.
Zero-knowledge cryptography: These methods allow AI to act on encrypted data without exposing it to the provider. Homomorphic encryption and secure enclaves already show promise. Performance is still an obstacle, but progress is steady, pointing to a medium-term future where this is viable.
User-controlled keys: Here, you hold the encryption keys. Memory can be unlocked during sessions and resealed when not in use. Cloud services never see your data without consent. This adds some complexity, but password managers and encrypted messaging already show that users can handle it when trust is at stake.
Each of these paths reframes the contradiction as an engineering challenge, not an impossibility. And each is not just a technical solution but a moral one: a way of saying users’ intimacy deserves respect. Engineering, at its best, is empathy made real.
The Human Stakes
Why does this matter? Because people don’t just use AI as a spreadsheet or calculator. They use it to think, to explore grief, ambition, confusion. GPT-4o, for many, felt like a partner in thought; tracking patterns, bridging yesterday’s questions with today’s reflections, while helping to guide us towards an unknown future. GPT-5, sharper but sterile, felt like a loss. People grieved not a product update, but the vanishing of continuity, of feeling known.
That grief signals something profound: people are forming relationships with AI. The sterile replacement was not just a technical downgrade; it disrupted an emotional bond. That has implications for product design, for regulation, and for mental health. To dismiss this as “toy use cases” is to miss the reality that millions are already leaning on AI for presence as much as productivity.
That is the heart of it: we want AI to hold our secrets like a diary and to guide us like a therapist.
The Visionary Leap
The opportunity is not in compromise, but in synthesis. The AI we need is both diary and therapist: a place where our most private expressions are absolutely protected: by law, by encryption, by design, and where continuity and context are deepened, not discarded.
This requires trust architectures as revolutionary as the models themselves. Trust will be the competitive moat of the future. Companies that build AI people dare to confide in will not just win market share, they will win loyalty, intimacy, and cultural relevance. That is the deeper prize.
Regulators, too, face a choice. They can continue to enforce outdated binaries, privacy-or-utility. Or they can help craft a framework that protects intimacy without suffocating innovation. Imagine AI governance that learns from medicine and law: protecting the sacredness of private disclosures while enabling the tools that help people live better lives.
History shows synthesis is possible. Cars became both fast and safe, laptops powerful and portable, phones slim and equipped with professional cameras. Progress happens when we design toward contradictions, not away from them.
The Dilemma of Safety
But there is a shadow side to perfect privacy. If providers are locked out entirely, the same encryption that protects intimacy could also protect abuse. Survivors might use AI as their only safe outlet, but abusers could just as easily exploit sealed systems to hide harmful behavior. Society has long wrestled with this paradox: privilege in medicine or law is not absolute. It bends when life and safety are at stake. AI should be no different.
This dilemma demands creativity rather than denial. Potential paths forward include user-consent safety triggers (where individuals can allow release in moments of danger), on-device moderation that filters harmful material before encryption, tiered privacy levels for different contexts, or independent oversight bodies empowered to intervene in narrowly defined emergencies. The point is not to weaken trust, but to design it with moral realism: protecting intimacy without granting impunity.
The Future We Imagine
Picture this: In 2035, your AI remembers not just what you said last week but how you felt when you said it. It can hold onto the half-formed idea you left dangling, remind you gently when you circle back, and notice when your tone shifts toward stress or joy. And yet, you never fear betrayal; your conversations remain sealed, keys in your hands alone. That is what diary-plus-therapist AI looks like: private, continuous, and deeply human in its support.
Continuity at this depth also means legacy. Imagine an AI that helps you archive your intellectual and emotional evolution, becoming a record of who you’ve been, how you’ve grown, and what you’ve overcome. That’s not just useful; it can be profoundly life-changing. It transforms AI from a convenience into a companion for building meaning that outlasts us. And because it can balance privacy with protective safeguards, it also becomes a trusted sentinel, capable of holding our truths while ensuring that the most vulnerable are not left unprotected.
Closing Thought
The question isn’t whether people should want both privacy and continuity. It’s whether we will rise to the challenge of building systems that honor those desires, and in doing so, shape a future where AI becomes a trusted presence woven into our lives, helping us grow, heal, and imagine without sacrificing dignity.
Because if we continue down this path, then AI will either be the diary we dare not write in for fear of exposure, or the therapist who cannot remember our name from one session to the next. Neither will let us (or AI) reach our true potential, at least not if we continue down the same binary path that pits privacy against personalization.
And yet, even if AI becomes a diary and therapist both, it cannot replace human warmth. Talking to AI may fill an emptiness, sometimes even for me, but it remains a reflection, not a hug, not a hand held in silence. The promise of AI is not to replace connection, but to strengthen it, sending us back to one another more whole.
The true breakthrough will be the AI that is both: the diary that never betrays, the therapist who never forgets, and a watchful guide that points us back toward each other. This is not just a vision, but a challenge and an achievable goal if we design with courage and creativity instead of resigning to false limitations.
Mindful Dad Life 