System Prompt

Peter

System Prompt is a podcast about what’s actually happening in AI. Not hype. Not surface-level takes. We break down how AI is changing software, SaaS, infrastructure, and the way systems are built focusing on real-world tradeoffs, architecture decisions, and where the value is actually shifting. If you’re building, deploying, or thinking seriously about AI, this is for you.

  1. 5d ago

    This Is Why Copilot Adoption Is Failing

    READ THE FULL EPISODE PAGE https://devmesh.tech/podcast/why-businesses-dont-trust-ai-agents-yet AI agents do not earn trust because they work once. They earn trust by working consistently, recovering from failure, and completing the workflows employees depend on. In Episode 19 of System Prompt, Peter and Val explore building business agents with Microsoft 365 and Copilot Studio. Peter demonstrates a COO-style agent grounded in SharePoint data. It reviews operational information, identifies risks, and hands report creation to a specialized subagent. Then the report-writing agent fails. The episode becomes a real-time look at what happens when an agent that worked previously suddenly stops, returns a generic system error, and gives the user no clear recovery path. WHAT WE DISCUSS • Building agents with Microsoft 365 • Grounding agents in SharePoint data • Using subagents for specialized tasks • Limiting tool surfaces • Running evaluations and reviewing traces • Troubleshooting failed agent handoffs • Comparing Copilot Studio with Claude Cowork • Why reliability affects adoption KEY TAKEAWAYS MICROSOFT OFFERS A PRACTICAL STARTING POINT For businesses already using Microsoft 365, Copilot Studio reduces some of the work around authentication, permissions, distribution, and access to business data. The opportunity is not just another AI model. It is an agent operating inside the environment employees already use. AGENTS NEED REAL RESPONSIBILITIES The COO Coach reviews operational information, identifies risks, prepares weekly summaries, and delegates report creation. That connects the agent to a real business process instead of using it as a general chatbot. SEPARATION OF DUTIES MATTERS The main agent handles analysis. The report-writing agent creates the final document. Smaller tool surfaces make workflows easier to understand, test, and troubleshoot. AI SHOULD SUPPORT HUMAN DECISIONS The agent identifies overdue invoices, missed milestones, declining margins, and other risks. It provides information and options without making executive decisions for the user. CONSISTENCY CREATES TRUST The workflow had worked several times before the episode. Nothing meaningful changed, but it began returning a generic system error during the live demo. It eventually worked again after settings were changed, saved, changed back, and saved again. That is not a dependable recovery process. When a three-minute workflow suddenly requires 25 minutes of troubleshooting, the value disappears. Consistency creates trust. Trust creates adoption. RELIABILITY IS THE USER EXPERIENCE Employees will not depend on an agent that works unpredictably before a meeting, review, or deadline. The manual process may be slower, but users will return to it when it is more dependable. A capable agent is not enough. It also has to work when people need it. CHAPTERS 00:00 — Introduction 01:04 — Building Agents with Microsoft 365 03:40 — Creating the COO Coach 11:37 — Live Copilot Studio Demo 18:45 — Troubleshooting and Evaluations 31:59 — Copilot Studio vs. Claude Cowork 34:24 — Consistency and Reliability 40:06 — Impact on Real Workflows 47:49 — Loss of Confidence 50:14 — Why Trust Drives Adoption 59:41 — Call for Better Reliability ABOUT SYSTEM PROMPT System Prompt covers AI infrastructure, automation, agents, enterprise platforms, training, and practical implementation.

  2. Jul 15

    Your Company Bought AI. Why Isn’t Anyone Using It?

    READ THE FULL EPISODE PAGE https://devmesh.tech/podcast/why-no-one-uses-your-ai Buying AI tools does not create adoption. Employees need to understand why the tools matter, how they connect to their work, and what success is supposed to look like. In Episode 18 of System Prompt, Peter and Val are joined by Jonny Havey of E-Learning Partners to discuss AI adoption, employee education, training ROI, and the role of clear KPIs. The conversation examines why training programs fail when they are disconnected from business outcomes and how organizations can measure whether AI education is actually improving performance. WHAT WE DISCUSS • Why buying AI tools does not guarantee adoption • The role of education in successful implementation • Why training must connect to business outcomes • How KPIs measure training effectiveness • Why ROI is broader than direct financial return • How AI is changing workplace education • Why employees need role-specific training • The difference between intelligence and wisdom KEY TAKEAWAYS AI ADOPTION REQUIRES MORE THAN ACCESS Giving employees an AI license does not mean they will use it effectively. They need to understand which problems it can solve, when it should be used, what data is appropriate to provide, and how outputs should be evaluated. TRAINING NEEDS CLEAR KPIS A training program should be tied to a measurable outcome. That could include faster task completion, fewer errors, reduced onboarding time, higher adoption, stronger confidence, or improved customer experience. The KPI should be defined before the training is created. ROI IS NOT ONLY FINANCIAL Training may create value through saved time, reduced risk, stronger consistency, better retention, and improved decision-making. Those outcomes may affect revenue or cost later, even when the return is not immediately financial. TRAINING SHOULD ALIGN WITH ORGANIZATIONAL GOALS Generic AI workshops often fail because they are disconnected from the work employees actually perform. Different teams have different workflows, risks, and responsibilities. Training becomes more useful when it is built around real business processes and expected outcomes. AI SHOULD SUPPORT WISDOM, NOT JUST OUTPUT AI can generate information quickly, but information alone does not create good judgment. Employees still need the experience and context required to evaluate recommendations and understand consequences. CHAPTERS 00:00 — The Importance of ROI and KPIs 05:16 — Aligning Training Programs with KPIs 08:36 — The Evolution of E-Learning Partners 24:19 — The Impact of AI Adoption on Education 45:42 — The Role of E-Learning Partners ABOUT JONNY HAVEY Jonny Havey is the co-founder of E-Learning Partners and host of the Learning Transformed podcast. https://elearningpartners.com WATCH THE EPISODE https://youtu.be/M3iRj99gy7Y ABOUT SYSTEM PROMPT System Prompt covers AI infrastructure, automation, agents, enterprise platforms, training, and practical implementation.

  3. Jul 8

    How AI is Transforming Food Discovery with Stupid Good AI

    READ THE FULL EPISODE PAGE https://devmesh.tech/podcast/food-discovery-stupid-good-ai Restaurant discovery is still dominated by ratings, generic lists, and search results that often fail to reflect what a person actually wants. In Episode 17 of System Prompt, Peter and Val are joined by David Leibner, founder of Stupid Good AI, to discuss how artificial intelligence could create a more personalized way to discover food, restaurants, and local experiences. The conversation explores how Stupid Good AI combines community feedback, data science, and AI to move beyond static reviews and broad “best of” lists. David also discusses building a vertical AI product, the changing software-development process, open models, and how user behavior can improve recommendations over time. WHAT WE DISCUSS • Why restaurant discovery still feels broken • How AI can personalize food recommendations • Why ratings alone do not capture individual preferences • How community feedback improves discovery • Building a vertical AI product around a specific problem • The role of data science and open models • How AI is changing software development • Turning local restaurant data into useful recommendations KEY TAKEAWAYS FOOD DISCOVERY NEEDS MORE CONTEXT A highly rated restaurant is not automatically the right recommendation for every person. Useful discovery should consider taste, location, occasion, dietary needs, budget, atmosphere, and previous preferences. COMMUNITY DATA CAN IMPROVE RECOMMENDATIONS User feedback gives the system more than a single star rating. Over time, patterns across reviews, preferences, and behavior can help produce recommendations that are more relevant to each user. VERTICAL AI PRODUCTS CAN GO DEEPER Stupid Good AI focuses on one specific problem rather than trying to become a general-purpose assistant. That narrower scope creates an opportunity to build better data, workflows, and experiences around food discovery. AI DOES NOT REPLACE PRODUCT THINKING Models can support recommendations, classification, search, and personalization. The product still needs strong data, useful interfaces, reliable feedback loops, and a clear understanding of the user’s problem. ABOUT DAVID LEIBNER David Leibner is the founder of Stupid Good AI, a platform focused on improving how people discover restaurants and local food experiences. Learn more: https://stupidgood.ai Early-access users can mention System Prompt when contacting the Stupid Good AI team. ABOUT SYSTEM PROMPT System Prompt covers AI infrastructure, automation, agents, local models, enterprise platforms, and practical implementation.

  4. Jul 1

    Enterprise AI Training Is Failing Because Tool Training Is Not Enough

    READ THE FULL EPISODE PAGE https://devmesh.tech/podcast/enterprise-ai-training Enterprise AI training is often reduced to showing employees how to use a chatbot or write better prompts. That is not enough for real implementation. In Episode 16 of System Prompt, Peter and Val examine why businesses need a deeper understanding of AI tools, ecosystems, workflows, memory, data, and operational responsibility. The conversation explores the role of a head of AI, forward-deployed engineers, and internal leaders who can connect business problems with practical systems. AI education should not be treated as a one-time workshop. Models, tools, risks, and capabilities change continuously, which means organizations need an ongoing process for learning, testing, and implementation. WHAT WE DISCUSS • Why basic prompt training is not enough • Starting with business problems instead of tools • Understanding AI ecosystems and integrations • The role of memory and context • Why employees need role-specific training • What a head of AI should own • How forward-deployed engineers support implementation • The gap between experimentation and production KEY TAKEAWAYS TOOL TRAINING IS NOT AI EDUCATION Teaching employees how to open a chatbot may create familiarity, but it does not explain how AI fits into workflows, where the risks are, what data can be used, or how outputs should be verified. START WITH THE BUSINESS PROBLEM Organizations should identify slow, expensive, repetitive, or error-prone processes before selecting an AI tool. The goal is to improve an outcome, not simply add AI. ECOSYSTEM UNDERSTANDING MATTERS AI tools interact with data, identity systems, permissions, applications, APIs, memory, retrieval, and existing workflows. Businesses need people who understand how those components connect and where failures may appear. THE HEAD OF AI IS AN OWNERSHIP ROLE A head of AI should connect business priorities, education, governance, implementation, measurement, and technical teams. Without clear ownership, adoption becomes fragmented across departments. FORWARD-DEPLOYED ENGINEERS CLOSE THE GAP These engineers work closely with users, processes, and existing infrastructure to turn business problems into working systems. Their value comes from combining technical execution with operational understanding. AI EDUCATION MUST CONTINUE Organizations need ongoing training, testing, documentation, and feedback. The goal is not to make every employee an AI expert. It is to make each employee competent within the boundaries of their role. CHAPTERS 00:00 — The Current State of Enterprise AI Education 07:27 — Identifying Problems and Solutions 19:01 — The Need for Deep Understanding 25:09 — The Role of Forward-Deployed Engineers 36:21 — The Challenge of AI Implementation WATCH THE EPISODE https://youtu.be/HdcDCQS1aRE ABOUT SYSTEM PROMPT System Prompt covers AI infrastructure, automation, agents, enterprise platforms, training, and practical implementation.

  5. Jun 25

    What Fable 5’s Removal Reveals About AI Model Dependence

    READ THE FULL EPISODE PAGE https://devmesh.tech/podcast/fable-5-removal-model-dependence What happens when a model your workflow depends on is suddenly removed, restricted, or made more expensive? In Episode 15 of System Prompt, Peter and Val discuss what Fable 5’s removal reveals about dependence on a single AI provider or model. The conversation explores alternative models, open models, operating costs, and the mistaken belief that openly available models are automatically free to use. A model may be downloadable without a per-token fee, but running it still requires hardware, hosting, power, engineering, maintenance, and monitoring. The broader lesson is that businesses should evaluate AI systems based on sustainable economics, portability, and the cost of completing real work. WHAT WE DISCUSS • The impact of Fable 5’s removal • The risks of depending on one model provider • Why businesses need model alternatives • Open models compared with proprietary frontier models • Why openly available models are not free to operate • Hardware, hosting, and maintenance costs • Sustainable economics for AI products • How open models can support proprietary products KEY TAKEAWAYS MODEL ACCESS CAN CHANGE Providers can remove models, alter subscription access, introduce limits, change pricing, or replace one model with another. Production workflows need fallback options and a clear migration path. ALTERNATIVE MODELS CREATE RESILIENCE Teams should understand which tasks require frontier capability and which can be handled by smaller, local, or openly available models. Routing and evaluation make it easier to move workloads when access or economics change. OPEN MODELS ARE NOT FREE TO OPERATE Organizations still need to account for hardware, cloud hosting, energy, storage, engineering, updates, security, monitoring, and support. The correct comparison is total cost per completed task. ECONOMICS SHOULD DRIVE MODEL SELECTION The most capable model is not always the most efficient model. Businesses should compare output quality, retries, latency, human review, integration effort, and operating cost. OPEN MODELS CAN POWER PROPRIETARY PRODUCTS A company can build commercial value around an open model through its data, workflow, integrations, evaluations, user experience, and operational system. The model may be open while the complete product remains differentiated. WATCH THE EPISODE https://youtu.be/xQ3RtLr3_t4 ABOUT SYSTEM PROMPT System Prompt covers AI infrastructure, automation, agents, local models, enterprise platforms, and practical implementation.

  6. Jun 17

    AI Security Goes Beyond Prompt Injection

    READ THE FULL EPISODE PAGE https://devmesh.tech/podcast/ai-security-beyond-prompt-injection AI security involves more than filtering bad prompts. In Episode 14 of System Prompt, Peter and Val examine the expanding attack surface created by large language models, AI agents, tools, retrieval systems, and external integrations. The discussion covers prompt injection, jailbreaks, system prompt extraction, context poisoning, supply-chain attacks, MCP and tool poisoning, sensitive information disclosure, and the defensive controls needed to reduce risk. The central point is simple: no system prompt or single filter can secure an AI application by itself. WHAT WE DISCUSS • Direct and indirect prompt injection • Skeleton key and crescendo jailbreaks • Context compliance attacks • System prompt extraction • Context and retrieval poisoning • Supply-chain attacks • Tool and MCP poisoning • Sensitive information disclosure • Instruction hierarchy and policy enforcement • Observability, testing, and defensive frameworks KEY TAKEAWAYS PROMPT INJECTION IS ONLY ONE ATTACK PATH Malicious instructions can enter through user input, retrieved documents, webpages, emails, tool responses, memory, or external integrations. Security must cover the entire pipeline, not only the chat interface. UNTRUSTED DATA SHOULD NOT BECOME INSTRUCTIONS AI systems combine system rules, user requests, retrieved content, and tool output. The system must distinguish trusted instructions from untrusted information. Retrieved documents should be treated as data, not authority. TOOLS INCREASE THE CONSEQUENCES OF FAILURE A compromised model response becomes more dangerous when the system can access files, send messages, modify records, execute commands, or call outside services. Tools need least-privilege permissions, strict schemas, validation, and approval boundaries outside the model. OBSERVABILITY IS A SECURITY REQUIREMENT Teams need visibility into prompts, retrieved context, routing, tool calls, permissions, outputs, and failures. Without tracing, it may be impossible to determine whether a bad result came from the model, poisoned context, or an unsafe integration. SECURITY REQUIRES LAYERS Useful defenses include access controls, input handling, output validation, sandboxing, allowlists, retrieval filtering, rate limits, testing, monitoring, and human approval for high-risk actions. No single control will stop every attack. CHAPTERS 00:00 — Celebrating Episode 14 05:14 — Prompt Injection and Defense 12:47 — Crescendo Jailbreak 17:51 — Context Compliance Attacks 34:24 — System Prompt Extraction 41:27 — Supply-Chain Attacks 48:20 — Sensitive Information Disclosure WATCH THE EPISODE https://youtu.be/X2UCeQQVWtc ABOUT SYSTEM PROMPT System Prompt covers AI infrastructure, automation, agents, local models, enterprise platforms, security, and practical implementation.

  7. Jun 12

    Model Routing Is the Hidden System Behind Reliable AI Applications

    READ THE FULL EPISODE PAGE https://devmesh.tech/podcast/model-routing Most AI applications are not powered by one model handling every request the same way. Behind the interface, routing logic decides which model, tool, workflow, or fallback should handle each task. In Episode 13 of System Prompt, Peter and Val examine model routing and why it has such a large impact on reliability, cost, consistency, security, and user experience. The conversation covers deterministic routing, regular expressions, model selection, tracing, silent reroutes, fallback behavior, and the small improvements that turn a basic AI workflow into a dependable product. WHAT WE DISCUSS • What model routing is • Why AI applications use multiple routes • Deterministic routing compared with model-based routing • Using regular expressions and rules to classify requests • Routing work to different models and tools • Why tracing and observability matter • How silent reroutes affect the user experience • The relationship between routing and output quality KEY TAKEAWAYS ROUTING DETERMINES WHAT HAPPENS NEXT A system may send simple work to a smaller model, complex work to a frontier model, sensitive work to a local model, or structured tasks to deterministic software. The route affects quality, speed, privacy, and cost. DETERMINISTIC ROUTING CREATES CONTROL Not every routing decision needs another AI model. Rules, keywords, regular expressions, permissions, and workflow state can provide predictable routing for clearly defined requests. MODEL-BASED ROUTING HANDLES AMBIGUITY Some requests cannot be classified reliably through fixed rules. A model can evaluate intent, complexity, or required capability and select an appropriate route. That flexibility still needs testing and clear boundaries. OBSERVABILITY IS ESSENTIAL Teams need to know which route was selected, what tools were called, how long the request took, and whether the result succeeded. Without tracing, routing failures can look like model failures. ROUTING IMPROVES INCREMENTALLY Reliable routing is rarely designed perfectly at the beginning. Small changes to rules, thresholds, fallbacks, and model selection can improve the full AI experience over time. CHAPTERS 00:00 — Introduction to Model Routing 08:14 — Types of Routing Systems 18:28 — Tracing and Observability 26:21 — Key Aspects of Routing Logic WATCH THE EPISODE https://youtu.be/DBcnHlZaM9Q ABOUT SYSTEM PROMPT System Prompt covers AI infrastructure, automation, agents, local models, enterprise platforms, and practical implementation.

  8. Jun 3

    The AI Hardware Shift: When Local Inference Starts Making Business Sense

    READ THE FULL EPISODE PAGE https://devmesh.tech/podcast/ai-hardware-shift AI capability is not determined by models alone. Hardware, memory, power, software support, and inference costs all shape what businesses can realistically deploy. In Episode 12 of System Prompt, Peter and Val examine the shift from cloud-based AI toward device-level and locally hosted inference. The conversation covers NVIDIA DGX Spark, CUDA, Apple silicon, RTX-class laptops, AMD Strix Halo, and the growing range of hardware available to small teams and mid-sized businesses. The central question is not whether local AI is better than cloud AI. It is when owning the hardware becomes more efficient than paying for every model call. WHAT WE DISCUSS • How hardware affects AI workloads • The shift from cloud inference to local processing • NVIDIA DGX Spark and the CUDA ecosystem • Apple silicon compared with NVIDIA-powered laptops • AMD Strix Halo and Gorgon Halo • Local inference for small teams • API costs and metered model usage • Routing work between local and cloud models • When hardware investment makes financial sense KEY TAKEAWAYS HARDWARE SHAPES WHAT AI SYSTEMS CAN DO Memory capacity, bandwidth, power use, software compatibility, and throughput determine which models can run and how quickly they complete work. LOCAL INFERENCE CHANGES THE COST MODEL Cloud AI turns infrastructure into a recurring operating expense. Local inference requires a larger upfront investment, but repeated workloads may become cheaper once the hardware is already owned. The useful comparison is total cost per completed task over time. CLOUD AND LOCAL CAN WORK TOGETHER Businesses do not need one environment for every workload. Routine, private, or high-volume tasks may run locally, while more complex work is routed to frontier APIs. NVIDIA’S SOFTWARE ECOSYSTEM STILL MATTERS NVIDIA benefits from broad support across AI frameworks and tooling. That reduces deployment friction, but it can also create vendor dependence and higher hardware costs. AMD COULD EXPAND LOCAL AI OPTIONS AMD systems with large unified-memory configurations may make larger models available on smaller devices. Adoption still depends on drivers, framework compatibility, inference tools, and developer support. BUY HARDWARE FOR A WORKLOAD Businesses should estimate workload volume, model size, performance needs, expected lifespan, electricity, support, and cloud alternatives before investing. The most powerful device is not automatically the most efficient choice. CHAPTERS 00:00 — The Future of AI Workers 12:12 — Apple M5 and NVIDIA RTX Spark Laptops 21:10 — AMD Strix Halo and Gorgon Halo 26:12 — Small Teams and Local Device Optimization 33:25 — Hardware Investment at Scale 40:21 — Inference Cost and Capability WATCH THE EPISODE https://youtu.be/wogixf6S_64 ABOUT SYSTEM PROMPT System Prompt covers AI infrastructure, automation, agents, local models, enterprise platforms, and practical implementation.

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About

System Prompt is a podcast about what’s actually happening in AI. Not hype. Not surface-level takes. We break down how AI is changing software, SaaS, infrastructure, and the way systems are built focusing on real-world tradeoffs, architecture decisions, and where the value is actually shifting. If you’re building, deploying, or thinking seriously about AI, this is for you.

Information

  • Creator
    Peter
  • Years Active
    2026
  • Episodes
    19
  • Rating
    Explicit
  • Copyright
    © 2026 Peter
  • Show Website
    System Prompt