<![CDATA[System Prompt]]>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.

]]>https://riverside.comRiverside.fm (https://riverside.com)Thu, 28 May 2026 05:57:21 GMTWed, 18 Mar 2026 14:05:04 GMT60Peter<p>System Prompt is a podcast about what’s actually happening in AI.</p><p>Not hype. Not surface-level takes.</p><p></p><p>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.</p><p></p><p>If you’re building, deploying, or thinking seriously about AI, this is for you.</p>episodicPeterpgreen@devmesh.techyes<![CDATA[AI in regulated industries]]>The conversation delves into the challenges and impact of AI in regulated industries, emphasizing the importance of doing what's right and unlocking value while balancing innovation and compliance. It explores the ethical and legal implications of AI, the risks of overestimating AI capability, and the impact of AI on legal processes. Additionally, it discusses the training and responsibility in AI, the role of junior employees in AI management, and the impact of AI on human-to-human interaction. Finally, it addresses the future of AI and legal responsibility, the impact of AI on legal discovery, and the role of Privileg in AI oversight, while balancing experimentation and legal oversight. The conversation delves into the transformative impact of AI in unlocking opportunities, addressing legal liability and model bias in fintech, the implications of government AI and regulation, the significance of Chat GPT, and rapid-fire Q&A on AI sectors, regulatory misconceptions, and advice for founders.

Takeaways

  • The importance of ethical and compliant AI implementation
  • The need for training and responsibility in AI usage AI's game-changing impact on opportunities
  • Legal liability and model bias in fintech

Chapters

  • 00:00 AI in Regulated Industries
  • 06:36 Ethical and Legal Implications of AI
  • 16:27 The Future of AI and Legal Responsibility
  • 23:49 Unlocking Opportunities with AI
  • 33:00 Government AI and Regulation
  • 40:08 Chat GPT and Regulatory Implications
]]>2d7c9643-0d9b-42c4-ac07-8969c75d648dWed, 27 May 2026 16:48:02 GMT<p>The conversation delves into the challenges and impact of AI in regulated industries, emphasizing the importance of doing what's right and unlocking value while balancing innovation and compliance. It explores the ethical and legal implications of AI, the risks of overestimating AI capability, and the impact of AI on legal processes. Additionally, it discusses the training and responsibility in AI, the role of junior employees in AI management, and the impact of AI on human-to-human interaction. Finally, it addresses the future of AI and legal responsibility, the impact of AI on legal discovery, and the role of Privileg in AI oversight, while balancing experimentation and legal oversight. The conversation delves into the transformative impact of AI in unlocking opportunities, addressing legal liability and model bias in fintech, the implications of government AI and regulation, the significance of Chat GPT, and rapid-fire Q&amp;A on AI sectors, regulatory misconceptions, and advice for founders.</p><p></p><p>Takeaways</p><ul><li>The importance of ethical and compliant AI implementation</li><li>The need for training and responsibility in AI usage AI's game-changing impact on opportunities</li><li>Legal liability and model bias in fintech</li></ul><p></p><p>Chapters</p><ul><li>00:00 AI in Regulated Industries</li><li>06:36 Ethical and Legal Implications of AI</li><li>16:27 The Future of AI and Legal Responsibility</li><li>23:49 Unlocking Opportunities with AI</li><li>33:00 Government AI and Regulation</li><li>40:08 Chat GPT and Regulatory Implications</li></ul>no00:46:59111AI in regulated industriesfull<![CDATA[Is AI Native hype? ]]>In this episode, Val and Peter discuss the concept of being AI native, exploring the challenges and misconceptions surrounding AI native builders and AI native products. They delve into the need for deterministic structures and processes in AI native products, the role of traditional software engineering practices, and the importance of planning and research in building AI native products. The conversation delves into the reality of building AI-native products and the role of AI in traditional systems. It emphasizes the importance of understanding the process and demystifying the sensationalism around AI-native products.

Takeaways

  • AI native products require deterministic structures and processes to ensure consistent and credible outputs.
  • AI native builders leverage AI to accelerate product development while maintaining traditional software engineering practices. AI-native builders are more than just traditional software engineers and should be seen as systems architects.
  • The term 'AI-native product' is more about marketing and sensationalism than a true representation of the product.

]]>61e6939b-b812-44d2-969a-e7f997dff5dfWed, 20 May 2026 16:48:10 GMT<p>In this episode, Val and Peter discuss the concept of being AI native, exploring the challenges and misconceptions surrounding AI native builders and AI native products. They delve into the need for deterministic structures and processes in AI native products, the role of traditional software engineering practices, and the importance of planning and research in building AI native products. The conversation delves into the reality of building AI-native products and the role of AI in traditional systems. It emphasizes the importance of understanding the process and demystifying the sensationalism around AI-native products.</p><p></p><p>Takeaways</p><ul><li>AI native products require deterministic structures and processes to ensure consistent and credible outputs.</li><li>AI native builders leverage AI to accelerate product development while maintaining traditional software engineering practices. AI-native builders are more than just traditional software engineers and should be seen as systems architects.</li><li>The term 'AI-native product' is more about marketing and sensationalism than a true representation of the product.</li></ul><p></p><p></p>no00:46:41110Is AI Native hype? full<![CDATA[AI in Business - Using the Right Tool for the Right Problem]]>The conversation delves into the challenges and opportunities of leveraging AI in business, particularly in the context of inventory management and customer-facing chatbots. It emphasizes the importance of understanding the problem, ensuring that AI solutions provide more value than cost, and building trust and empathy in AI implementation.

Takeaways

  • Understanding the problem is crucial
  • AI solutions should provide more value than cost
  • Empathy and trust are essential in AI implementation

Chapters

  • 00:00 Leveraging AI in Business
  • 06:46 Point of Sale Systems vs. AI
  • 13:36 Tailored AI Solutions for Businesses
  • 19:18 Customer-Facing Chatbots
  • 30:20 Data Cleaning for AI Implementation
]]>870df677-703a-4c55-83bd-6423018eb328Wed, 13 May 2026 17:30:00 GMT<p>The conversation delves into the challenges and opportunities of leveraging AI in business, particularly in the context of inventory management and customer-facing chatbots. It emphasizes the importance of understanding the problem, ensuring that AI solutions provide more value than cost, and building trust and empathy in AI implementation.</p><p></p><p>Takeaways</p><ul><li>Understanding the problem is crucial</li><li>AI solutions should provide more value than cost</li><li>Empathy and trust are essential in AI implementation</li></ul><p></p><p>Chapters</p><ul><li>00:00 Leveraging AI in Business</li><li>06:46 Point of Sale Systems vs. AI</li><li>13:36 Tailored AI Solutions for Businesses</li><li>19:18 Customer-Facing Chatbots</li><li>30:20 Data Cleaning for AI Implementation</li></ul>no00:39:0219AI in Business - Using the Right Tool for the Right Problemfull<![CDATA[Episode 8: Prompt Engineering vs RAG vs Finetuning]]>The conversation covers the importance of prompt engineering, the role of prompting in AI model performance, the use of keyword search for refining AI outputs, and the introduction to Retrieval Augmented Generation (RAG) for further refinement. The conversation delves into the technical aspects of data storage, canonicalization, and the use of MariaDB for vector store and operational data. It emphasizes the importance of efficiency and cost considerations in refining RAG systems and the need for human involvement in AI models. The discussion also explores the purpose and benefits of fine-tuning AI models, an iterative approach to AI model development, scaling, system integration, and the future of AI technologies.

Takeaways

  • Prompting is crucial for AI model performance
  • Keyword search and RAG are important for refining AI outputs Canonicalization and normalization reduce the amount of embedded logs by 70%
  • Fine-tuning AI models requires a clear understanding of the desired output and iterative testing

Chapters

  • 00:00 Introduction to Prompt Engineering
  • 07:15 Using Keyword Search
  • 13:00 Introduction to RAG
  • 24:59 Data Storage and Canonicalization
  • 33:10 Understanding Fine-Tuning of AI Models
  • 40:18 Iterative Approach to AI Model Development
  • 49:54 Edge Technologies and Future of AI
]]>2de17ffa-dce9-4dda-abbb-2018934abf37Wed, 06 May 2026 16:18:13 GMT<p>The conversation covers the importance of prompt engineering, the role of prompting in AI model performance, the use of keyword search for refining AI outputs, and the introduction to Retrieval Augmented Generation (RAG) for further refinement. The conversation delves into the technical aspects of data storage, canonicalization, and the use of MariaDB for vector store and operational data. It emphasizes the importance of efficiency and cost considerations in refining RAG systems and the need for human involvement in AI models. The discussion also explores the purpose and benefits of fine-tuning AI models, an iterative approach to AI model development, scaling, system integration, and the future of AI technologies.</p><p></p><p>Takeaways</p><ul><li>Prompting is crucial for AI model performance</li><li>Keyword search and RAG are important for refining AI outputs Canonicalization and normalization reduce the amount of embedded logs by 70%</li><li>Fine-tuning AI models requires a clear understanding of the desired output and iterative testing</li></ul><p></p><p>Chapters</p><ul><li>00:00 Introduction to Prompt Engineering</li><li>07:15 Using Keyword Search</li><li>13:00 Introduction to RAG</li><li>24:59 Data Storage and Canonicalization</li><li>33:10 Understanding Fine-Tuning of AI Models</li><li>40:18 Iterative Approach to AI Model Development</li><li>49:54 Edge Technologies and Future of AI</li></ul>no00:50:3218Episode 8: Prompt Engineering vs RAG vs Finetuningfull<![CDATA[Episode 7 : AI News Today]]>The conversation covers the degradation of AI model quality, the impact of API costs, and the dynamics of competition and market trends in the AI industry. It delves into the challenges faced by companies like Anthropic and OpenAI, as well as the implications for enterprise users and the broader AI ecosystem.

]]>4ca535bd-0329-4677-a175-03a2f296febbWed, 29 Apr 2026 16:23:16 GMT<p>The conversation covers the degradation of AI model quality, the impact of API costs, and the dynamics of competition and market trends in the AI industry. It delves into the challenges faced by companies like Anthropic and OpenAI, as well as the implications for enterprise users and the broader AI ecosystem.</p><p></p><p></p>no00:40:0317Episode 7 : AI News Todayfull<![CDATA[Episode 6: AI & Ethics]]>The conversation delves into the ethical considerations of AI implementation, its impact on workplace productivity, and the reshaping of jobs. It also explores the role of AI in decision-making, critical thinking, and education. The need for responsible AI implementation and the importance of AI literacy and training are highlighted throughout the discussion.

Takeaways

  • Responsible AI implementation
  • Ethical considerations in AI
  • Impact of AI on education and workplace productivity

]]>430c7fb8-ac6c-4d53-b1c1-f173e77b89bfWed, 22 Apr 2026 16:36:25 GMT<p>The conversation delves into the ethical considerations of AI implementation, its impact on workplace productivity, and the reshaping of jobs. It also explores the role of AI in decision-making, critical thinking, and education. The need for responsible AI implementation and the importance of AI literacy and training are highlighted throughout the discussion.</p><p></p><p>Takeaways</p><ul><li>Responsible AI implementation</li><li>Ethical considerations in AI</li><li>Impact of AI on education and workplace productivity</li></ul><p></p><p></p>no00:40:2316Episode 6: AI & Ethicsfull<![CDATA[Episode 5: Rise of Physical AI]]>The conversation delves into the rise of physical AI, exploring its applications in controlled environments, challenges in navigating novel scenarios, and the ethical considerations of human-robot interaction. It also discusses the impact of physical AI on society and the future of this technology, highlighting the limitations and costs associated with its implementation.

Takeaways

  • Physical AI operates within controlled environments
  • Challenges in navigating novel scenarios
  • Human-robot interaction and ethical considerations

Chapters

  • 00:00 The Rise of Physical AI
  • 05:39 Under the Hood: How Physical AI Works
  • 10:55 The Role of Vision in AI
  • 20:19 The Future of Physical AI
  • 26:06 The Cost of Physical AI
]]>9ec890b5-3f04-4cb3-8ae3-cdaf6a08945dWed, 15 Apr 2026 17:00:02 GMT<p>The conversation delves into the rise of physical AI, exploring its applications in controlled environments, challenges in navigating novel scenarios, and the ethical considerations of human-robot interaction. It also discusses the impact of physical AI on society and the future of this technology, highlighting the limitations and costs associated with its implementation.</p><p></p><p>Takeaways</p><ul><li>Physical AI operates within controlled environments</li><li>Challenges in navigating novel scenarios</li><li>Human-robot interaction and ethical considerations</li></ul><p></p><p>Chapters</p><ul><li>00:00 The Rise of Physical AI</li><li>05:39 Under the Hood: How Physical AI Works</li><li>10:55 The Role of Vision in AI</li><li>20:19 The Future of Physical AI</li><li>26:06 The Cost of Physical AI</li></ul>yes00:35:0715Episode 5: Rise of Physical AIfull<![CDATA[Episode 4: Write Apps Right Tools]]>The conversation delves into the concept of agentic coding, its impact on software development, the importance of planning, and the changing role of developers. It also explores the future of software development and the user experience, emphasizing the need for a shift in mindset and skill set for developers.

Takeaways

  • Agentic coding is a workflow replacement, not just a tool upgrade.
  • The shift to agentic coding requires a shift in mindset and skill set for developers.

]]>7a9f4b43-5451-42cd-b2c3-748b0e248dc4Wed, 08 Apr 2026 16:13:08 GMT<p>The conversation delves into the concept of agentic coding, its impact on software development, the importance of planning, and the changing role of developers. It also explores the future of software development and the user experience, emphasizing the need for a shift in mindset and skill set for developers.</p><p></p><p>Takeaways</p><ul><li>Agentic coding is a workflow replacement, not just a tool upgrade.</li><li>The shift to agentic coding requires a shift in mindset and skill set for developers.</li></ul><p></p><p></p>yes00:36:5114Episode 4: Write Apps Right Toolsfull<![CDATA[Are AI Agents taking jobs?]]>The conversation delves into the impact of AI agents on job roles and the shift in job responsibilities. It explores the definition of AI agents, their role in software engineering, the effect of capital expenditure on job stability, task transformation, decrease in junior positions, and workforce exposure to AI. It also discusses the redefinition of roles, opportunities for small agencies, the impact on translation and language services, and the evolution of the IT industry through ServiceNow. The conversation delves into the impact of AI on jobs, work-life balance, and the future of industries. It explores the need for adaptability and upskilling in the face of AI's influence. The discussion also addresses the costs and benefits of AI implementation and the societal and economic implications of AI.

Takeaways

  • AI agents are impacting job roles
  • Shift in job roles due to AI agents AI's impact on jobs and industries
  • The need for adaptability and upskilling

Chapters

  • 00:00 ServiceNow and the IT Industry Evolution
  • 31:59 The Future of AI and Job Displacement
  • 39:25 Costs and Benefits of AI Implementation
  • 45:21 Societal and Economic Implications of AI
]]>99755ef1-c2c9-4bd4-a460-f1342015915fWed, 01 Apr 2026 18:37:20 GMT<p>The conversation delves into the impact of AI agents on job roles and the shift in job responsibilities. It explores the definition of AI agents, their role in software engineering, the effect of capital expenditure on job stability, task transformation, decrease in junior positions, and workforce exposure to AI. It also discusses the redefinition of roles, opportunities for small agencies, the impact on translation and language services, and the evolution of the IT industry through ServiceNow. The conversation delves into the impact of AI on jobs, work-life balance, and the future of industries. It explores the need for adaptability and upskilling in the face of AI's influence. The discussion also addresses the costs and benefits of AI implementation and the societal and economic implications of AI.</p><p></p><p>Takeaways</p><ul><li>AI agents are impacting job roles</li><li>Shift in job roles due to AI agents AI's impact on jobs and industries</li><li>The need for adaptability and upskilling</li></ul><p></p><p>Chapters</p><ul><li>00:00 ServiceNow and the IT Industry Evolution</li><li>31:59 The Future of AI and Job Displacement</li><li>39:25 Costs and Benefits of AI Implementation</li><li>45:21 Societal and Economic Implications of AI</li></ul>no00:51:0713Are AI Agents taking jobs?full<![CDATA[Opensource vs Frontier Models]]>The conversation delves into the comparison between local and frontier LLM models, highlighting the impact of curation on model execution. It explores the future of local models, the implications of security and ownership, the potential of AI in home automation, and the considerations for businesses when choosing between frontier and local models. The conversation delves into the comparison between curation and pre-trained raw models, the importance of orchestration and pipeline curation, the impact of local models on infrastructure costs, the considerations of privacy and cost, and the future of local models and AI integration.

Takeaways

  • Local models vs. frontier models
  • Curation shapes execution Curation vs Pre-trained Raw Models
  • Business-specific Compliance Needs

Chapters

  • 00:00 Local vs. Frontier LLM Models
  • 08:38 Future of Local Models
  • 17:00 Security and Ownership in Local Models
  • 23:03 Business Decision: Frontier vs. Local Model
  • 32:03 Orchestration and Pipeline Curation
  • 42:38 Privacy and Cost Considerations
]]>a8e96705-3086-42bd-bd95-eddb6a5ff224Wed, 25 Mar 2026 16:57:13 GMT<p>The conversation delves into the comparison between local and frontier LLM models, highlighting the impact of curation on model execution. It explores the future of local models, the implications of security and ownership, the potential of AI in home automation, and the considerations for businesses when choosing between frontier and local models. The conversation delves into the comparison between curation and pre-trained raw models, the importance of orchestration and pipeline curation, the impact of local models on infrastructure costs, the considerations of privacy and cost, and the future of local models and AI integration.</p><p></p><p>Takeaways</p><ul><li>Local models vs. frontier models</li><li>Curation shapes execution Curation vs Pre-trained Raw Models</li><li>Business-specific Compliance Needs</li></ul><p></p><p>Chapters</p><ul><li>00:00 Local vs. Frontier LLM Models</li><li>08:38 Future of Local Models</li><li>17:00 Security and Ownership in Local Models</li><li>23:03 Business Decision: Frontier vs. Local Model</li><li>32:03 Orchestration and Pipeline Curation</li><li>42:38 Privacy and Cost Considerations</li></ul>no00:50:0612Opensource vs Frontier Modelsfull<![CDATA[SaaS vs AI Agents]]>The conversation delves into the evolving landscape of AI and its impact on Software as a Service (SaaS). It explores the shift in value from software to AI, the challenges and considerations of building internal tools, and the future of SaaS in the era of AI. The complexities of AI integration, the impact on product people, and the need for innovation and adaptation are also highlighted.

Takeaways

  • The shift in value from software to AI is reshaping the landscape of Software as a Service.
  • The complexities of building and maintaining internal tools and infrastructure in the era of AI.

Chapters

  • 00:00 AI vs. Software as a Service
  • 06:22 Ownership and Responsibility
  • 11:55 AI Implementation and Adoption
  • 19:29 The Future of Software as a Service
  • 32:43 Building Internal Tools and Infrastructure
]]>df68d631-bbfb-4d1a-89f1-640fdc22a8ddThu, 19 Mar 2026 17:14:52 GMT<p>The conversation delves into the evolving landscape of AI and its impact on Software as a Service (SaaS). It explores the shift in value from software to AI, the challenges and considerations of building internal tools, and the future of SaaS in the era of AI. The complexities of AI integration, the impact on product people, and the need for innovation and adaptation are also highlighted.</p><p></p><p>Takeaways</p><ul><li>The shift in value from software to AI is reshaping the landscape of Software as a Service.</li><li>The complexities of building and maintaining internal tools and infrastructure in the era of AI.</li></ul><p></p><p>Chapters</p><ul><li>00:00 AI vs. Software as a Service</li><li>06:22 Ownership and Responsibility</li><li>11:55 AI Implementation and Adoption</li><li>19:29 The Future of Software as a Service</li><li>32:43 Building Internal Tools and Infrastructure</li></ul>yes00:43:3111SaaS vs AI Agentsfull