You’re about to get the INSANE Framework for Creating Voice AI Prompts (Prompt Engineering Guide) by Henryk Brzozowski, a practical playbook forged from 300+ handcrafted prompts and 50+ voice production systems. It lays out the four pillars, prompt v1–v3, testing processes, and advanced flows so you can build prompts that work reliably across LLMs without costly fixes.
The video’s timestamps map a clear workflow: problem framing, pillar setup, iterative prompt versions, testing, context management, inbound/outbound tips, and final best practices. Use this guide to craft, test, and iterate voice prompts that perform in production and save you time and money.
Problem Statement and Why Most Voice AI Prompts Fail
You build voice AI systems because you want natural, efficient interactions, but most prompts fail before you even reach production. The problem isn’t only model capability — it’s the gap between how you think about text prompts and the realities of voice-driven interfaces. When prompts break, the user experience collapses: misunderstandings, incorrect actions, or silent failures make your system feel unreliable and unsafe. You need a structured approach that treats voice as a first-class medium, not as text with a microphone tacked on.
Common misconceptions after watching a single tutorial
After a single tutorial you might assume prompts are simple: write a few instructions, feed them to a model, and it works. In reality, tutorials hide messy details like ASR errors, conversational context, timing, and multimodal signals. You learn an elegant pattern on stage but don’t see the brittle assumptions behind it — such as perfect transcription or single-turn interactions. Expecting tutorial-level simplicity often leads you to under-engineer error handling and overestimate production readiness.
Typical failure modes in production voice systems
In production you’ll see failure modes such as misrecognized intents due to ASR errors, truncated or overly long replies, repeated clarification loops, and hallucinations where the model invents facts or actions. You’ll also encounter latency spikes when prompts demand heavy context, and brittle logic when prompts don’t handle interruptions, overlapping speech, or partial utterances. Each failure mode has user-facing consequences: frustration, mistrust, and possible safety risks.
Differences between text prompts and voice-first prompts
Text prompts assume perfectly typed input, visible context, and user tolerance for longer content. Voice-first prompts must handle disfluencies, lack of punctuation, overlapping speakers, and the need for brevity. You must also consider TTS constraints, timing for turn-taking, and multimodal signals like touch or visual context. A prompt that works for chat will often fail in voice because it doesn’t address these operational realities.
Cost and time consequences of broken prompts
Broken prompts cost you in engineering hours, escalated customer support, user churn, and wasted compute. If you don’t catch failures early, you’ll pay for model calls to troubleshoot and fix issues in high-stakes environments. Fixing a failing prompt in production can mean rewrites, long regression tests, and expensive A/B cycles — sometimes at a contractor rate that makes the whole product economically unviable.
Why handcrafting and iteration matter
Handcrafting initial prompts and iterating quickly on them is essential because it surfaces real-world edge cases and failure patterns early. You learn what users actually say, how ASR performs, and which constraints the model ignores. Iteration lets you harden behavior, add guardrails, and measure improvements. The upfront work saves you time and money later, because you reduce the amount of post-deployment firefighting and create predictable behavior.
Four Pillars That Underpin Reliable Voice Prompts
You want prompts that behave predictably. The four pillars below are foundational principles that guide prompt design and lifecycle: intent fidelity, robustness, clarity, and evaluation. Each pillar addresses a different risk area, and together they give you a durable framework for voice-first interactions.
Pillar: Intent fidelity — capturing and preserving user intention
Intent fidelity means your system accurately recognizes what the user intended and preserves that meaning through processing and action. To achieve this, you must explicitly represent goals, required slots, and success criteria in your prompt so the model aligns its output with real user outcomes. That prevents misinterpretation and reduces unnecessary clarifications.
Pillar: Robustness — handling noise, interruptions, and edge input
Robustness covers resilience to ASR errors, background noise, user disfluency, and unexpected utterances. Build redundancies: confidence thresholds, fallback flows, retry strategies, and explicit handling for partial or interrupted speech. Robust prompts anticipate poor inputs and provide safe default behaviors when signals are ambiguous.
Pillar: Clarity — unambiguous directions for the model
Clarity means your prompt leaves no room for vague interpretation. You define role, expected format, allowed actions, and prohibited behavior. A clear prompt reduces hallucinations, minimizes variability, and supports easier testing because you can write deterministic checks against expected outputs.
Pillar: Evaluation — measurable success criteria and monitoring
Evaluation ensures you measure what matters: intent recognition accuracy, successful task completion, latency, and error rates. You instrument the system to log confidence scores, user corrections, and key events. Measurable criteria let you judge prompt changes objectively rather than relying on subjective impressions.
How the four pillars interact in voice-first scenarios
These pillars interact tightly: clarity helps fidelity by defining expectations; robustness preserves fidelity under noisy conditions; evaluation exposes where clarity or robustness fail. In voice-first scenarios, you can’t prioritize one pillar in isolation — a clear but brittle prompt still fails if ASR noise is pervasive, and a robust prompt that isn’t measurable can hide regressions. You design prompts to balance all four simultaneously.
Introducing the INSANE Framework (Acronym Breakdown)
INSANE is a practical acronym that maps to the pillars and provides a step-by-step mental model for building prompts that work in voice systems. Each letter points to a focused area of prompt engineering that you can operationalize and test.
I: Intent — specify goals, context, and desired user outcome
Start every prompt by making the user’s goal explicit. Define success conditions and what “complete” means. Include contextual details that influence intent: user role, prior actions, and available capabilities. When the model understands the intent precisely, its responses will align better with user expectations.
N: Noise management — strategies for ASR errors and ambiguous speech
Anticipate transcription errors by including noise-handling strategies in the prompt: ask for confirmations when confidence is low, normalize ambiguous inputs, and prefer safe defaults. Use ASR confidence and alternative hypotheses (n-best lists) as inputs so the model can reason about uncertainty instead of assuming a single perfect transcript.
S: Structure — main prompt scaffolding and role definitions
Structure is the scaffolding of the prompt: a role declaration (assistant/system/agent), a context block, instructions, constraints, and output schema. Clear structure helps the model prioritize information and reduces unintended behaviors. Use consistent sections and markers so you can automate parsing, versioning, and testing.
A: Adaptivity — handling state, personalization, and multi-turn logic
Adaptivity covers how prompts handle conversational state, personalization, and branching logic. You must include signals for session state, user preferences, and how to escalate or change behavior over multiple turns. Design the prompt to adapt based on stored metadata and to gracefully handle mismatches between expectation and reality.
N: Normalization — canonicalizing inputs and outputs for stability
Normalize inputs (lowercasing, punctuation, slot canonicalization) and outputs (consistent formats, canonical dates, IDs) before and after model calls. Normalization reduces the surface area for errors, simplifies downstream parsing, and ensures consistent behavior across user variants.
E: Evaluation & safety — metrics, guardrails, and fallback behavior
Evaluation & safety integrate your monitoring and protective measures. Define metrics to track and guardrails to prevent harm — banned actions, sensitive topics, and data-handling rules. Include explicit fallback instructions the model should follow on low confidence, such as asking a clarifying question or transferring to human support.
How INSANE maps onto the four pillars
INSANE maps directly to the four pillars: Intent and Structure reinforce intent fidelity and clarity; Noise management and Normalization fortify robustness; Adaptivity and Evaluation & safety ensure you can measure and maintain reliability. The mapping shows the framework isn’t theoretical — it ties each practical step to the core reliability goals.
Main Structure for Voice AI Prompts
You’ll want a repeatable template for each prompt. Consistent structure helps with versioning, testing, and handoffs between engineers and product managers. The following blocks are the essential pieces you should include in every voice prompt.
Role and persona: establishing voice, tone, and capabilities
Define the role and persona at the top of the prompt: who the assistant is, the tone to use, what it can and cannot do. For voice, specify brevity, empathy, or assertiveness and how to handle interruptions. This helps the model align to brand voice and sets user expectations.
Context block: what to include and how much history to pass
Include only the context necessary for the current decision: recent user utterances, session state, and relevant long-term preferences. Avoid passing entire histories verbatim; instead, provide summarized state and key facts. This preserves token budgets while retaining decision-critical information.
Instruction block: clear, actionable directives for the model
Your instruction block should be concise and actionable: what task to perform, the steps to take, and how to prioritize subgoals. Make instructions specific (e.g., “If date is ambiguous, ask a single clarifying question”) to limit model creativity that causes errors.
Constraints and safety: limits, banned behaviors, and format rules
List hard constraints like privacy policies, topics to avoid, and disallowed actions. Also include format rules: maximum sentence length, forbidden words, or whether the assistant should avoid giving legal or medical advice. These constraints are your programmable safety net.
Output specification: exact shapes, markers, and response types
Specify the exact output shape: JSON schema, labeled fields, or plain text markers. For voice, include response types (short reply, SSML, action directive) and markers for actions (e.g., [CALL_API], [CONFIRM]). A rigid output spec makes downstream processing deterministic.
Example block: minimal few-shot examples for desired behavior
Provide a few minimal examples that demonstrate correct behavior, covering common happy paths and a couple of failure modes. Keep examples short and representative to bias the model toward the patterns you want to see without overwhelming it.
Prompt Versioning and Iterative Design
You need a versioning and iteration strategy to evolve prompts safely. Treat prompts like code: branch, test, and document changes so you can roll back quickly when an update causes regression.
Prompt v1: rapid prototyping with simple instruction sets
Prompt v1 is minimal: role, intent, and one or two example interactions. Use v1 for rapid exploration and to gather real user utterances. Don’t over-engineer — early iterations should prioritize speed and coverage of common flows.
Prompt v2: adding context, constraints, and edge-case handling
Prompt v2 incorporates context, basic noise-handling rules, and constraints discovered during prototyping. Here you add handling for ambiguous phrases, simple fallback logic, and more precise output formats. This is where you reduce hallucination and tighten behavior.
Prompt v3: production-hardened prompt with safety and observability
Prompt v3 is production-ready: comprehensive safety checks, robust normalization, logging hooks for observability, and explicit fallback strategies. You also instrument metrics and add monitoring triggers for threshold-based rollbacks. v3 should have been stress-tested with simulated noise and adversarial inputs.
Version control approaches: naming, diffing, and rollback strategies
Name prompts with semantic versioning and brief changelogs embedded in the prompt header. Keep diffs small and well-documented, and store prompts in a repository so you can diff and rollback. Use feature flags to phase rollouts and quickly revert if you detect regressions.
A/B testing prompts and tracking performance changes
Run A/B tests when you change major behaviors: measure task completion, user satisfaction, clarification rates, and error metrics. Track both model-side and ASR-side metrics to isolate the source of change. Use statistical thresholds to decide whether a new prompt is an improvement.
Testing Process and Debugging Voice Prompts
Testing voice prompts requires simulating real conditions and having robust debugging steps that isolate problems across prompt, model, and ASR layers.
Automated test cases: canonical utterances and adversarial inputs
Build automated suites with canonical utterances (happy paths) and adversarial inputs (noisy, ambiguous, malicious). Automation checks output formats, action triggers, and key success criteria. Run these tests on each prompt change and on model upgrades.
Human-in-the-loop evaluation: labeling and qualitative checks
Use human raters to label correctness, fluency, and safety. Qualitative reviews catch subtle issues automation misses, such as tone mismatches or confusing clarification strategies. Regular human review cycles keep the system aligned with user expectations.
Simulating ASR errors and noisy channels during testing
Introduce simulated ASR errors: misrecognized words, dropped phrases, and timing jitter. Use n-best lists and confidence shifts to see how your prompt responds. Testing under noisy channels reveals brittle logic and helps you build practical fallbacks.
Metrics to monitor: success rate, intent recognition, hallucination rate
Monitor task success rate, intent classification accuracy, clarification frequency, and hallucination rate. Also track latency and TTS issues. Set SLAs and alert thresholds so you’re notified when behavior deviates from expected ranges.
Debugging steps: isolating prompt vs. model vs. ASR failures
When something breaks, isolate the layer: replay raw audio through ASR, replay transcripts to the model, and run the prompt in a controlled environment. If ASR introduces errors, focus on preprocessing and noise handling; if the model misbehaves, refine prompt structure or examples; if the prompt is fine but model outputs are inconsistent, consider temperature settings or model upgrades.
Context Management and Conversation State
Managing context is vital in voice systems because you have limited tokens and varied session types. Decide what to persist and how to summarize to maintain continuity without bloating requests.
Session vs. long-term memory: what to persist and when to purge
Persist ephemeral session details (recent slots, active task) for the conversation and reserve long-term memory for stable preferences (language, accessibility settings). Purge sensitive or stale data proactively and implement retention policies that protect privacy and reduce context bloat.
Techniques for summarization and context compression
Use summarization to compress multi-turn history into concise state representations. Summaries should capture intent, solved tasks, and unresolved items. Apply extraction for structured data (slots) and generate short natural-language summaries for model context.
Chunking strategy for very long histories
Chunk long histories into prioritized segments: recent turns first, then relevant older segments, and finally a compressed summary of the remainder. Use heuristics to drop low-importance details and keep the token footprint manageable.
Context windows and token budgets: prioritization heuristics
Design prioritization heuristics that favor immediate context and high-signal metadata (e.g., active task, user preferences). When token budgets are tight, prefer structured facts and summaries over raw transcripts. Monitor token usage to prevent latency spikes.
Storing metadata and signal flags to guide behavior
Store metadata such as ASR confidence, user corrections, and whether the user explicitly opted into a preference. Use simple flags to instruct the model (“low_confidence”, “user_requested_human”) so behavior adapts without reprocessing full histories.
Input Design for Voice-First Systems
Your input pipeline shapes everything downstream. You must design preprocessing steps and choose whether to extract slots up front or let the model handle free-form comprehension.
ASR considerations: transcripts, confidence scores, and timestamps
Capture full transcripts, n-best alternatives, token-level confidence, and timestamps. These signals let your prompt and downstream logic reason about uncertainty and timing, which is essential for handling interruptions and partial commands.
Preprocessing: normalization, punctuation, and disfluency removal
Normalize transcripts by fixing casing, inserting punctuation heuristically, and removing filler words where appropriate. Preprocessing reduces ambiguity and helps the model parse meaningful structure from spoken language.
Slot extraction vs. free-form comprehension approaches
Decide whether to extract structured slots via rules or NER before the model call, or to let the model parse free-form inputs. Slot extraction gives you deterministic fields for downstream logic; free-form comprehension is flexible but requires stronger prompt instructions and more testing.
Handling non-verbal cues and system prompts in multi-modal setups
In multi-modal systems, include non-verbal cues (button presses, screen taps) and system prompts as part of context. Non-verbal signals can disambiguate intent and should be represented as structured events in the prompt input stream.
Designing utterance collection for robust training and tests
Collect diverse utterances across accents, noise conditions, and phrasing styles. Annotate with intent, slots, and error patterns. A well-designed dataset speeds up prompt iteration and helps you reproduce production failures in test environments.
Output Design and Voice Response Generation
How the model responds — both in content and format — determines user satisfaction. Make outputs deterministic where possible and design graceful fallbacks for low-confidence situations.
Specifying response format: short replies, multi-part actions, JSON
Specify the response format explicitly. Use short replies for confirmations, multi-part actions for complex flows, or strict JSON when downstream systems rely on parsed fields. Structured outputs reduce downstream parsing complexity.
TTS friendliness: pacing, phonetic guidance, and SSML use
Design responses for TTS: control pacing, provide phonetic spellings for unusual names, and use SSML to manage pauses, emphasis, and prosody. TTS-friendly outputs improve perceived naturalness and comprehension.
Fallbacks and graceful degradations for low-confidence answers
On low confidence, favor safe fallbacks: ask a clarifying question, offer alternatives, or transfer to human support. Avoid guessing when the cost of an incorrect action is high. Your prompt should encode escalation rules.
Controlling verbosity and verbosity-switch strategies
Control verbosity with explicit rules: default to concise replies, escalate to detailed responses when asked. Include a strategy to switch verbosity (e.g., “If user says ‘explain’, provide a longer answer”) so the system matches user intent.
Post-processing outputs to enforce safety and downstream parsing
After model output, run deterministic checks: validate JSON, sanitize personal data, and ensure no banned behaviors were suggested. Post-processing is your final safety gate before speaking to the user or invoking actions.
Conclusion
You now have a complete playbook to approach voice prompt engineering with intention and discipline. The INSANE framework and four pillars give you both strategic and tactical guidance to design prompts that survive real-world noise and scale.
Recap of the INSANE framework and four pillars
Remember: Intent, Noise management, Structure, Adaptivity, Normalization, Evaluation & safety (INSANE) map onto the four pillars of intent fidelity, robustness, clarity, and evaluation. Use them together — they’re complementary, not optional.
Key operational practices to move prompts into production
Operationalize prompts through versioning, automated tests, human-in-the-loop evaluation, and clear observability. Prototype quickly, then harden through iterations and rigorous testing under realistic voice conditions.
Next steps: testing, measurement, and continuous improvement
Start by collecting real utterances, instrumenting metrics, and running small A/B tests. Iterate based on data, and keep your rollout controlled with feature flags and rollback plans. Continuous improvement is what turns a brittle demo into a trusted product.
Encouragement to iterate and build observability around prompts
Voice systems are messy, but with structured prompts and an observability-first mindset you can build reliable experiences. Keep iterating, listen to user signals, and invest in monitoring — the improvements compound fast and make your product feel remarkably human.
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