Ben AI · Published 2026-09-02

Anthropic Just Revealed 7 New Rules for Prompting Claude 5 Models

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claude 5promptinganthropicopus 5fable 5agentic systemsprompt engineeringlong-horizon agentstoken economicssystem promptsguardrailsSelf-verification
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Summary

Overview

  • Speaker: Ben
  • Channel: Ben AI
  • Main topic: New prompting rules and best practices for Claude 5 and Fable 5 models based on Anthropic's latest guides and keynotes
  • Purpose: To educate professionals and business owners on how to adapt their prompting strategies for Claude 5 models to avoid burning tokens, reduce hallucinations, and maximize model capabilities. The video breaks down seven essential rules for prompting Claude 5 models (including Opus 5 and Fable 5), explaining how these newer models function fundamentally differently than older versions. Instead of step-by-step micro-prompting, users must provide complete job specifications, utilize pre-planning strategies like the interview-me skill, explain the 'why' behind requests, define clear exit criteria, swap hard prohibitions for positive reasons, avoid redundant double-checking, and lock down output voice once.

Topic Map

Introduction and Why Claude 5 Needs New Prompting

  • Explanation: Introduction to Anthropic's new prompting guides and keynotes, explaining that Claude 5 models (Opus 5, Fable 5, etc.) operate differently from previous iterations and require a shift in prompting methodology.
  • Key claims:
    • Almost everyone is prompting Claude 5 models wrong.
    • Claude 5 models are trained to execute end-to-end tasks.
    • We need to change how we prompt in 2026.
  • Examples:
    • Claude Opus 5, Fable 5, Sonnet, and Haiku models working fundamentally differently than previous models.
  • Terminology:
    • Claude 5
    • Claude Opus 5
    • Fable 5
    • agentic coding
  • Why it matters: Using outdated micro-prompting techniques with advanced models leads to poor results and wasted tokens.

Rule 1: Give The Whole Job

  • Explanation: Instead of prompting step-by-step, give Claude 5 the complete task specification upfront and let it run left-to-right, as these models are trained for end-to-end execution.
  • Key claims:
    • Claude 5 models perform best when given the complete task specification upfront.
    • They are trained specifically on executing end-to-end tasks.
  • Examples:
    • Giving Claude the job, why, guardrails, and done means all in one prompt for a YouTube video outline.
  • Terminology:
    • The Job
    • The Why
    • The Guardrails
    • Done Means
  • Why it matters: Upfront complete task specifications leverage the model's training on long-horizon autonomous tasks.

Rule 2: Interview Yourself First

  • Explanation: When tasks are complex or specifications are unclear, use the 'Interview Me' skill to have Claude ask clarifying questions before generating the final prompt or output.
  • Key claims:
    • Pre-planning and clarifying unknowns leads to better outputs and reduces iteration loops.
    • The Interview Me skill uncovers unknowns in user requests.
  • Examples:
    • Using Interview Me for planning a personal analytics dashboard.
  • Terminology:
    • Interview Me skill
    • Map and Territory
    • Known Unknowns
  • Why it matters: Investing time in pre-planning saves tokens and prevents endless iteration cycles.

Rule 3: Why, Not Just What

  • Explanation: Provide the intent and broader context behind requests so the model can make better micro-decisions on complex tasks.
  • Key claims:
    • Claude Fable 5 performs better when it understands the intent behind a request.
    • Understanding the bigger picture helps the model make unprescribed decisions correctly.
  • Examples:
    • Explaining why a YouTube video is being made for non-technical professionals.
  • Terminology:
    • intent behind the request
    • context-budget concern
  • Why it matters: Complex tasks require the model to make unpredicted judgment calls guided by intent.

Rule 4: Define Done

  • Explanation: Clearly define exit criteria and output styles to prevent long-running models from over-delivering and consuming excess tokens.
  • Key claims:
    • Claude 5 models are long-running and tend to do too much rather than too little.
    • Defining exact exit criteria prevents token burning.
  • Examples:
    • Specifying 'A pre-outline for this video that means 8 to 15 practical tips...'.
  • Terminology:
    • exit criteria
    • output style
    • long-horizon autonomy
  • Why it matters: Clear boundaries stop autonomous models from going off-track and wasting resources.

Rule 5: Reasons, Not Rules

  • Explanation: Swap hard negative prohibitions (e.g., 'never do this') for positive instructions backed by reasons.
  • Key claims:
    • New intelligent models respond far better to instructions combined with reasons.
    • Avoiding aggressive capitalization and strict prohibitions prevents over-triggering.
  • Examples:
    • Changing 'Avoid escalating...' to explaining why escalation costs money.
  • Terminology:
    • hard prohibitions
    • bare prohibitions
    • over-triggering
  • Why it matters: Smart models process and follow reasoned guidance more effectively than flat negative commands.

Rule 6: Avoid Double-Checking

  • Explanation: Avoid adding explicit verification steps or sub-agents into prompts because Claude 5 models are already trained to autonomously verify and self-correct.
  • Key claims:
    • Claude Opus 5 and Fable 5 verify their own work without being told.
    • Instructing explicit verification adds unnecessary cost without improving results.
  • Examples:
    • Removing instructions like 'include a final verification step' from prompt templates.
  • Terminology:
    • sub-agents
    • over-verification
    • self-correction
  • Why it matters: Redundant verification adds financial and token cost without boosting quality.

Rule 7: Fix The Voice Once

  • Explanation: Set global system instructions or project prompts to fix verbosity and voice issues once rather than repeating formatting constraints in every prompt.
  • Key claims:
    • Claude Opus 5 can become overly verbose and use heavy jargon.
    • Global instructions or system prompts fix voice issues consistently.
  • Examples:
    • Adding 'Keep responses focused, brief, and concise. Avoid jargon & being overly verbose' to global system instructions.
  • Terminology:
    • system prompt
    • verboseness
    • global instruction
  • Why it matters: Centralized voice control eliminates repetitive prompt clutter.

Key Points

Give the entire job upfront

  • Explanation: Claude 5 models excel when provided with complete specifications rather than step-by-step instructions.
  • Evidence: Anthropic prompting best practices documentation for Claude Opus 5 and Fable 5.
  • Practical implication: Restructure prompt templates to include job, why, guardrails, and done criteria all at once.

Use the Interview Me skill for complex tasks

  • Explanation: Before executing complex tasks, have Claude interview the user to surface unknown variables.
  • Evidence: Anthropic's internal workflow and Fable 5 usage guidelines.
  • Practical implication: Deploy the Interview Me skill to pre-plan tasks and generate precise briefs.

Provide reasons alongside instructions

  • Explanation: Modern models respond better to rationale than rigid negative rules.
  • Evidence: Comparison examples from Anthropic's prompting playbook.
  • Practical implication: Replace 'never do X' with 'do Y because Z'.

Frameworks, Models & Processes

The Full Job Brief Template

  • How it works: A four-part prompting framework designed for Claude 5 models that encompasses the core job, intent, boundaries, and completion criteria.
  • Components:
    • The Job
    • The Why
    • The Guardrails
    • Done Means
  • When to use: When initiating complex tasks or agentic workflows with Claude 5 models.

Examples & Case Studies

Ben used the Full Job Brief framework combined with Claude Fable 5 to generate a YouTube video outline.

  • Illustrates: Rule 1 (Give the Whole Job) and Rule 4 (Define Done).
  • Lesson: Providing a complete brief upfront produced a highly accurate and useful video outline without iterative back-and-forth.

Ben applied the Interview Me skill to plan a personal analytics dashboard.

  • Illustrates: Rule 2 (Interview Yourself First).
  • Lesson: Asking clarifying questions beforehand eliminates ambiguity and forms a solid brief.

Actionable Takeaways

  • Immediate:
    • Stop prompt-chaining step-by-step and provide the full job specification upfront.
    • Use system-level instructions to fix Claude's verbosity and voice once.
    • Replace strict prohibitions with reason-based instructions.
  • Strategic:
    • Adopt a pre-planning mindset, spending more time on upfront brief creation.
    • Leverage internal skills like Interview Me and Rule Rewriter to optimize prompts for newer models.
  • Questions to investigate:
    • How do these prompting rules scale across different API integrations and custom agent builds?
    • What are the precise cost-to-token savings when removing self-verification steps from long-horizon prompts?

Claims Worth Verifying

  • Claude 5 models perform best when given the complete task specification upfront and left to run. (factual)
  • Claude Opus 5 and Fable 5 are trained to autonomously verify and correct their own work. (factual)

Notable Quotes

"I recently looked at Anthropic's new guides and keynotes on prompting, and I learned that almost everyone is prompting Claude 5 models wrong." (at 0:00) "I think a really common mistake that I see is people are using Claude Code, they're using Claude and they just give it like way overly specific instructions." (at 1:56) "Claude Opus 5 verifies its own work without being told." (at 18:21)

Compressed Summary

  • Provide the full job, why, guardrails, and done criteria upfront.
  • Use Interview Me to uncover unknowns before task execution.
  • Explain the why behind instructions rather than using bare prohibitions.
  • Define strict exit criteria to prevent long-running model token waste.
  • Avoid redundant double-checking and self-verification prompts.
  • Fix output verbosity and voice globally in system instructions.
  • Keywords: claude 5, prompting, anthropic, opus 5, fable 5
  • Core insight: Claude 5 models require a mindset shift from micro-managing step-by-step prompts to acting as competent employees who need complete briefs, intent, and clear exit criteria.

Core insights

7
Mechanismmedium noveltymoderate evidence

For long-horizon Claude 5 models, explicitly decomposing a job into step-by-step micro-prompts is no longer the optimal control strategy. The model should receive a complete task specification — job, why, guardrails, and exit criteria — and execute end-to-end, because its training encodes the planning and sequencing that older prompt chains were used to add.

Why it matters

Changes where task responsibility sits: the harness/operator should specify goals and constraints rather than prescribe detailed execution steps. Adopting this can reduce interaction overhead and better use model capability, but it raises the cost of a single failed or off-track run.

Generalization

As foundation models become trained for long-horizon autonomy, the division of labor between prompter/orchestrator and model shifts from 'how to do it' toward 'what complete outcome is wanted and how to recognize done'.

Claude 5 models perform best when given the complete task specification upfront.
Open source video
They are trained specifically on executing end-to-end tasks.
Open source video
Practicemedium noveltymoderate evidence

Ambiguous tasks should trigger an explicit pre-planning interview phase (Anthropic's 'Interview Me' skill) before execution because pre-emptively surfacing known unknowns and clarifying user intent replaces expensive, error-prone iteration loops with a single precise brief.

Why it matters

This is a concrete orchestration pattern: distinguish specification-discovery from execution and make the first an explicit, explicit part of an agentic workflow. It also gives product builders a mechanism to reduce token burn from repeated failed attempts.

Generalization

Agent systems that need reliable long-horizon behavior should include an early, cheap dialog stage that estimates task ambiguity and asks only the highest-value questions before committing resources to execution.

The Interview Me skill uncovers unknowns in user requests.
Open source video
Investing time in pre-planning saves tokens and prevents endless iteration cycles.
Open source video
Mechanismmedium noveltymoderate evidence

The 'why' behind a request is not fluff; it is the basis for micro-decisions that the model will have to make without explicit instruction. An autonomous agent given intent can infer correct trade-offs for the many unprescribed choices it will face.

Why it matters

Agent task objects need to include an intent layer, not just requirements. When model outputs are judged, intent supplies an implicit value function that helps the model avoid locally correct but globally wrong behavior.

Generalization

For any sufficiently autonomous system, specifying intent as a first-class field is as important as specifying the objective function or reward signal in classical control.

Claude Fable 5 performs better when it understands the intent behind a request.
Open source video
Complex tasks require the model to make unpredicted judgment calls guided by intent.
Open source video
Failure Modemedium noveltymoderate evidence

Long-running Claude 5 models are biased toward over-delivering, not under-delivering. Without concrete exit criteria and output-style constraints, an autonomous agent can drift into extra work and burn tokens after the user's actual need was already satisfied.

Why it matters

In agentic architectures, termination and scope constraints are cost controls. Defining 'done' before launch is as important as defining the objective; otherwise, the system naturally overperforms at the user's expense.

Generalization

Any long-horizon autonomous system should include an explicit terminal predicate or 'definition of done' in its initial state to protect against positive drift.

Claude 5 models are long-running and tend to do too much rather than too little.
Open source video
Defining exact exit criteria prevents token burning.
Open source video
Mechanismhigh noveltymoderate evidence

Hard negative prohibitions ('never do X') degrade behavior on modern models; replacing them with positive instructions plus reasons reduces over-triggering and produces better compliance. The model must understand why to act responsibly, not simply recognize forbidden tokens.

Why it matters

Constraint encoding in prompts should be framed as values and rationale, not an authoritarian rule list. This has implications for safety guardrails and system prompts: rules hidden in caps and negations may actively trigger undesired defensive behavior.

Generalization

When writing constraints for complex learned systems, it can be more robust to teach the objective and reasoning behind a constraint than to use a long list of prohibited actions.

New intelligent models respond far better to instructions combined with reasons.
Open source video
Avoiding aggressive capitalization and strict prohibitions prevents over-triggering.
Open source video
Empirical Resulthigh noveltyweak evidence

Explicitly instructing verification or adding verification sub-agents to Claude 5 prompts is redundant and costs money: the newer models already self-verify and self-correct without being told to do so.

Why it matters

This is a surprising counter-argument to a common agent design pattern of adding verifier loops or 'final-check' steps. For capable models, such external checks may be a needless tax unless they provide an independent assurance layer.

Generalization

Agent designers should not assume more checking equals better output; evaluation should measure marginal quality benefit of added verifier sub-steps against their marginal cost.

Claude Opus 5 and Fable 5 verify their own work without being told.
Open source video
Instructing explicit verification adds unnecessary cost without improving results.
Open source video
Practicelow noveltymoderate evidence

Verbosity, jargon, and voice should be controlled once at the system-prompt level rather than repeated as formatting constraints in every task prompt. This reduces prompt clutter and produces more consistent output style across runs.

Why it matters

This is a clear system-boundary decision: global behavior belongs in global configuration, not in per-task user messages. Harness APIs should expose and enforce stable style/voice settings so task prompts contain only work-specific instructions.

Generalization

In any agent system, repeated per-call style constraints signal a missing platform-level configuration; centralizing them improves token usage and consistency.

Global instructions or system prompts fix voice issues consistently.
Open source video
Centralized voice control eliminates repetitive prompt clutter.
Open source video

Deep dives

4

Whole-job specification vs. step-by-step decomposition for long-horizon models

Research question

Across task classes, complexity levels, and model capability tiers, when does one-shot whole-job specification outperform step-by-step prompt decomposition in task success and token cost?

Why

This determines whether agent orchestrators should shift from procedural control to outcome specification, and where the break-even points are.

Claude 5 models perform best when given the complete task specification upfront.
Open source video
They are trained specifically on executing end-to-end tasks.
Open source video
Source video

Automated triggering of pre-execution interview phases based on task ambiguity

Research question

Can model-predicted ambiguity, user retry history, and task budget reliably predict when an interview/pre-planning phase reduces total tokens and iterations?

Why

An automatic router between 'ask clarifying questions' and 'execute directly' would realize the token savings of interview-me without burdening low-ambiguity tasks.

Investing time in pre-planning saves tokens and prevents endless iteration cycles.
Open source video
The Interview Me skill uncovers unknowns in user requests.
Open source video
Source video

Robustness of reason-based guardrails over hard prohibitions across model generations

Research question

Is the over-triggering effect of hard negative prohibitions stable across future model versions and safety-tuning data, or is it a transient post-training artifact?

Why

If the effect is durable, prompt and system-prompt engineering should encode constraints as values and rationale; if transient, rule-based prohibitions may become viable again.

Avoiding aggressive capitalization and strict prohibitions prevents over-triggering.
Open source video
New intelligent models respond far better to instructions combined with reasons.
Open source video
Source video

When external verification adds marginal value over model self-verification

Research question

For high-stakes, low-tolerance task distributions, does explicitly added verification improve measured output quality enough to justify its cost compared with relying on model self-verification?

Why

Agent design typically assumes more verification is always safer; evidence for when self-verification is sufficient can remove needless latency and tokens, and isolate high-value verifier placements.

Instructing explicit verification adds unnecessary cost without improving results.
Open source video
Claude Opus 5 and Fable 5 verify their own work without being told.
Open source video
Source video

Article ideas

4

Stop micromanaging your AI: why whole-job prompts beat step-by-step chains

For models trained on long-horizon autonomy, complete task specifications with job-why-guardrails-done will outperform step-by-step micro-prompting because decomposition is now embedded in training; therefore orchestration UIs should be redesigned for work orders, not implementation steps.

Angle

Engineering consequence: raise the level of control from method to outcome and update agent harnesses.

Source video

The interview isn't friction, it's a token optimizer

Asking clarifying questions before agentic execution is not a UX tax but the cheapest possible correctness lever: a short clarification phase prevents the costly loop of repeated failed execution, so product teams should build pre-run interviews into workflows that expect ambiguity.

Angle

Cost economics of pre-planning versus iteration loops.

Source video

Your verifier agent is probably redundant

Major model families now self-verify and self-correct internally, so bolting on explicit verification steps and verifier sub-agents mostly taxes the user; the design rule should be to add external verifiers only where an independent assurance layer is genuinely required.

Angle

Challenge to the default 'always verify' pattern in agent architectures.

Source video

Don't say no: the case for reason-based guardrails in agent prompts

Hard prohibitions fail twice, both by over-triggering defensive behavior at the wrong times and by failing to teach correct judgment; guardrails should instead be written as positive values plus rationale that the model can generalize across unanticipated cases.

Angle

A new prompt-authoring convention for safety-sensitive agent workloads.

Source video

Project ideas

3

SpecBench

beyond-evals

For a fixed set of long-horizon tasks, one-shot whole-job prompting achieves at least equal objective task success while consuming fewer interaction tokens and user turns than step-by-step decomposition prompting.

Proof of concept

Build a task suite from real-world long-horizon jobs; run each condition on the same model; score outputs against objective criteria automatically and log token/metadata.

Measurement

Task completion rate per condition, tokens per successful task, user edit/correction count.

Source video

AmbiguityRouter

movement-lab

A harness that triggers an interview phase only when model-predicted task ambiguity exceeds a threshold will reduce total user interaction time and token spend compared to both no-interview and always-interview policies.

Proof of concept

Build a thin orchestrator in front of a long-horizon model: it asks the model to score ambiguity, then conditionally emits clarifying questions before generating the full task spec.

Measurement

Mean time-to-completion, retries per task, user satisfaction score, tokens consumed.

Source video

DoneShield

gatehouse

Injecting machine-checkable exit criteria derived from the user's 'done means' into the system prompt will reduce token overspend from over-delivery without reducing user-rated completion quality.

Proof of concept

In an agent loop, parse the task spec for explicit exit predicates, expose them to the model and to a scheduler that checks for early stopping, then compare against a baseline with no exit guard.

Measurement

Token consumption per run, overrun rate (work beyond done), user-rated quality score.

Source video

Architectural implications

4

The model is capable of end-to-end execution when it receives the complete task specification upfront.

Before

Orchestrators decomposed user requests into small execution steps and fed each step to the model sequentially.

After

Orchestrators pass the whole job, why, guardrails, and done criteria as one work order, and then supervise the output rather than directing each step.

Consequence

Fewer round-trips and lower per-task cost, but the initial spec must be much richer and exit criteria must be explicit before the run starts.

Source video

Ambiguity is best resolved before execution via an interview/pre-planning phase.

Before

Missing details caused iterative re-prompting and retries during actual task execution.

After

The system runs a cheap clarification session up front, asking targeted questions, then generates a precise brief used as the execution spec.

Consequence

Higher-quality final outputs with fewer failed runs, at the price of adding a user interaction stage to every high-ambiguity task.

Source video

The model validates its own work internally and does not need explicit 'verify your output' instructions.

Before

Agent workflows inserted explicit verification loops, sub-agents, or final-check prompts after every generation.

After

Verification steps are removed by default and reserved for cases where independent assurance is necessary (e.g., high-stakes domains).

Consequence

Lower cost and latency, with a possible loss of external auditability unless a third-party verifier is added deliberately.

Source video

Voice and verbosity are properties of the whole agent, not single requests.

Before

Every task prompt repeated phrases like 'keep it concise, avoid jargon'.

After

System or project prompts set output style once; task prompts contain only content-level instructions.

Consequence

Less token clutter and more consistent formatting across tasks, but requires a well-maintained global prompt store.

Source video

Tradeoffs and failure modes

4

Complete upfront spec and model autonomy

Benefit

The model can execute end-to-end without manual step-by-step control and tends to finish the intended job.

Cost or risk

The same autonomy means the model over-delivers (does too much) and burns tokens if 'done means' is not sharply defined.

Claude 5 models are long-running and tend to do too much rather than too little.
Open source video
Source video

Pre-planning interview phase

Benefit

Clarifying unknowns before generation reduces iteration loops and wasted execution effort.

Cost or risk

The interview consumes up-front user time and tokens; if overused on trivial tasks it adds overhead instead of saving it.

Investing time in pre-planning saves tokens and prevents endless iteration cycles.
Open source video
Source video

Removing explicit verification instructions

Benefit

Avoids adding unnecessary cost because newer models already self-correct during runtime.

Cost or risk

For third-party guarantees, process auditability, or regulated workflows, invisible self-validation may not be enough, and the practice may not transfer to less capable models.

Instructing explicit verification adds unnecessary cost without improving results.
Open source video
Source video

Positive, reason-based instructions vs. hard prohibitions

Benefit

Reasoned positive guidance is followed better and does not over-trigger defensive behavior.

Cost or risk

Users are asked to supply concise rationale for every guardrail, which increases prompt-authoring effort and favors thoughtful design over quick rule lists.

Avoiding aggressive capitalization and strict prohibitions prevents over-triggering.
Open source video
Source video

Open questions

4

For which task sizes and model capability thresholds does giving the whole job upfront beat step-by-step decomposition?

Why unresolved

The summary presents the rule for Claude 5 but does not compare against alternative control strategies or measure error rates and cost on varied task classes.

Research direction

Benchmark long-horizon tasks across different levels of upfront specification vs. procedural decomposition, measuring correctness, token cost, and user effort.

Source video

How can a harness automatically decide when to invoke an interview/pre-planning phase versus when to proceed directly with the task?

Why unresolved

No deterministic heuristic for ambiguity is given; interview-accuracy depends on human judgment and the model's self-assessment.

Research direction

Study using model-predicted task ambiguity, budget constraints, and past user retry rates as signals for triggering an interactive clarification phase.

Source video

Does removing explicit verification maintain quality on high-stakes, low-tolerance tasks where silent internal self-correction cannot be audited?

Why unresolved

The claim is stated as a general truth but is not supported by task-level error data or reliability comparisons.

Research direction

Set up evaluation suites with a separate external verifier for revenue-critical or safety-critical generation and compare quality/cost against explicit verification prompts.

Source video

Will the observed 'over-triggering' from hard prohibitions persist as model training and alignment evolve, or is it a transient artifact of current post-training data?

Why unresolved

Over-triggering likely depends on safety tuning choices, which the summary does not explain.

Research direction

Run longitudinal studies across model versions using identical prohibition-style prompts and measure refusal/overkill rates.

Source video

Key claims

7
factualVerification needed

Claude 5 models are trained to execute end-to-end tasks.

Evidence

They are trained specifically on executing end-to-end tasks.

Question

Is this verified by public model-card specifications or controlled experiments on Claude 5 model variants?

Source video
comparativeVerification needed

Providing complete task specifications upfront produces better performance than step-by-step prompts.

Evidence

Claude 5 models perform best when given the complete task specification upfront.

Question

What evaluation benchmarks were used to compare one-shot full specifications against step-by-step prompting?

Source video
causalVerification needed

Investing time in pre-planning saves tokens and prevents endless iteration cycles.

Evidence

Investing time in pre-planning saves tokens and prevents endless iteration cycles.

Question

What controlled cost/quality measurements support this causal claim?

Source video
causalVerification needed

Claude Fable 5 performs better when it understands the intent behind a request.

Evidence

Claude Fable 5 performs better when it understands the intent behind a request.

Question

Does this result appear in Anthropic's prompting guide with quantitative comparisons?

Source video
comparativeVerification needed

Explicit verification instructions add unnecessary cost without improving results.

Evidence

Instructing explicit verification adds unnecessary cost without improving results.

Question

Across what task distribution and model versions was the delta measured? Is the effect monotonic as task complexity scales?

Source video
factualVerification needed

Claude Opus 5 and Fable 5 verify their own work without being told.

Evidence

Claude Opus 5 and Fable 5 verify their own work without being told.

Question

What experimental evidence demonstrates internal self-verification in these models?

Source video
causalVerification needed

Avoiding aggressive capitalization and strict prohibitions prevents over-triggering.

Evidence

Avoiding aggressive capitalization and strict prohibitions prevents over-triggering.

Question

What mechanism causes over-triggering, and is this a robust effect across safety-tuned model classes?

Source video

Connections

5