Part #2: Technical Building Blocks of GenAI & Agent Powered Products
Ten building blocks that form a robust, end-to-end map of how to build AI-driven products and agent-enabled solutions.
This is follow up of the post#1 where we demystifed five critical building blocks of AI products: Data Ingestion & Preparation, Data Labeling & Synthetic Data, Compute Infrastructure, Vector Databases, Orchestration Frameworks
Now, we turn our attention to the remaining five: Foundation Models, Fine-Tuning & Customization, Model Supervision & Observability, Model Safety & Responsible AI, and Agent Architectures & Tooling. These components are equally vital in creating a holistic approach to building effective AI-powered solutions.
Recap
Author's Note
As I myself embark on this journey of understanding the essential building blocks of AI products, I invite you to join me in this learning experience. By understanding these building blocks comprehensively, we as PMs can better navigate the complexities of AI technology and make informed decisions that drive our products forward.
Embrace this framework, (whether you a new or seasoned AI Product Manager) to can make informed decisions that align with your business goals and user needs.
These building blocks aren’t strictly linear—some blocks overlap or happen in parallel. What matters is shaping each area thoughtfully, anticipating challenges, and embedding sound engineering and product practices from day one.
Model Pipeline & Lifecycle
Block 6: Foundation Models
Foundation models are AI models pre-trained on extensive or multimodal datasets, capable of understanding and generating content at scale. They can be fine-tuned for specific tasks across various applications and serve as the "brains" of modern AI.
6.1 What Happens Here
Select a baseline large language model (LLM) or multimodal model (e.g., GPT, LLaMA).
Leverage its broad capabilities: text generation, Q&A, sentiment analysis, image recognition, and more.
Potentially combine multiple foundation models (e.g., text + vision) for advanced features.
6.1.1 Foundation Models vs. LLMs
Foundation Models are pre-trained AI systems that serve as a base layer, like an AI operating system. LLMs are a specific type of Foundation Model specialized for language tasks.
Foundation Models - Think of Foundation Models as versatile AI brains that can handle multiple types of tasks:
Text: Understanding and generating language
Images: Creating, editing, and understanding visuals
Audio: Processing speech and sounds
Video: Understanding and generating video content
Multi-modal: Combining different types of data (text + images + audio)
→ Examples: Meta's SEER (images), Google's PaLM (multi-modal), OpenAI's GPT-4 (multi-modal)
LLMs - LLMs are a specific type of Foundation Model that specializes in language tasks, like:
Writing and editing content
Answering questions
Translation
Code generation
Text summarization
→Examples: GPT-3.5, Claude, LLaMA
Foundation Models
✅ Use when:
Building multi-modal applications
Need flexibility across different AI tasks
Want a single model to power multiple features
LLMs
✅ Use when:
Focusing purely on language-based features
Need deep language understanding
Building text-centric products
6.2 What PMs Need to Know:
✅ Start with clear use cases rather than technology. Let your product needs drive the choice between a broader Foundation Model or a specialized LLM.
✅ Open-Source vs. Proprietary:
Open source allows deep customization and cost control but demands robust in-house expertise.
Closed-Source solutions (e.g., commercial APIs) speed time-to-market and reduce operational overhead.
✅ Data Privacy & Compliance: Foundation models can inadvertently capture or expose sensitive data.
✅ Performance vs. Cost: Large models with billions of parameters can skyrocket usage costs—plan budgets carefully!
✅ Determine your product possibilities and limitations, including accuracy requirements, privacy policies, and domain coverage.
6.3 Strategic Decisions
🤔 How do you integrate the model into your product architecture (e.g., API calls vs. on-prem deployment)?
🤔 Whether you need domain-specialized models or if general-purpose LLMs suffice.
⚖️ Long-term roadmap for model updates—foundation models evolve quickly.
6.4 Top 3 Challenges
❌ Data Quality Issues – Flawed or unrepresentative training data can lead to inaccuracies or bias.
❌ High Compute Costs – Large model inference can drive up operational expenses and latency.
❌ Hallucinations – Even the best LLMs may generate plausible, incorrect, or nonsensical answers.
6.5 Key Mitigations
👉 Optimize Compute Costs from the outset. Monitor usage and experiment with more minor or quantized variants.
👉 Use Synthetic Data to expand training sets without risking real user info.
👉 Integrate Fact-Checking or domain-specific trusted sources to catch hallucinations.
👉 Regularly evaluate model performance in real production scenarios
💡 Now, Let’s Recap here:
Model Pipeline & Lifecycle
Block 7: Fine-Tuning & Customization
Tailoring a general foundation model to your specific context: legal, finance, healthcare, or beyond the domain.
7.1 What Happens Here
Ingest domain-specific data and re-train or “fine-tune” your baseline model.
Adjust prompt engineering or user flows to reflect brand voice, compliance needs, or specialized tasks.
7.2 What PMs Need to Know
✅ Scope of Customization: Zero-shot, few-shot, or full fine-tuning?
✅ Budget for Ongoing Training: Especially if your domain is rapidly changing.
✅ User Feedback Loops: Observing real user interactions can guide iterative refinement.
7.3 Strategic Decisions
✅ Parameter Efficiency: Use techniques like LoRA (Low-Rank Adaptation) or Adapters to cut costs and time.
✅ Pilot Studies: Test small domain data sets before investing in large-scale fine-tuning.
✅ Version Management: Keep track of updated fine-tuned versions and roll back if needed.
7.4 Top 3 Challenges
❌ Overfitting – The model may lose general capabilities if domain data is too narrow.
❌Rising Costs – Repeated large-scale fine-tunings can become prohibitive.
❌ Complex Approval Processes – Each re-training might require compliance signoff in regulated domains.
7.5 Key Mitigations
👉 Use Parameter-Efficient Methods (prompt engineering, few-shot techniques) first.
👉 Budget & ROI Tracking: Ensure each fine-tuning cycle delivers measurable value.
👉 Systematic A/B Tests to confirm performance improvements vs. baseline.
💡 Now, Let’s Recap here:
Model Pipeline & Lifecycle
Block 8: Model Supervision & Observability
All AI in production drifts or degrades without real-time tracking. This block is about tracking performance, bias, reliability, and user experiences in real-time.
8.1 What Happens Here
Metrics dashboards for usage, latency, accuracy, user sentiment, and cost.
Drift detection algorithms to highlight shifts in data or model performance.
Just as you monitor a new hire's performance, you need to watch how your AI performs in the real world.
Drift Detection
Think of this as detecting when your AI starts "going off script":
If you trained it on formal business emails but users send casual texts
If product categories change but the model still uses old classifications
8.1.1 PM's Quick Guide
✅ Must Monitor Daily:
Accuracy scores
Response times
User satisfaction
Operational costs
❌ Red Flags:
Sudden accuracy drops
Increased user complaints
Unexpected responses
Rising costs
Set up automated alerts for the Observability metrics, just like you would for any critical product feature.
8.2 What PMs Need to Know
✅ Compliance Integration: Some domains (finance and healthcare) require regulated tracking of AI decision-making.
✅ User Feedback Channels: Mechanisms for users to flag incorrect or harmful outputs.
✅ Incident Response: Who investigates and remediates issues if the model goes off track?
8.3 Strategic Decisions
✅ Tools for model observability (e.g., Arize AI, WhyLabs, custom dashboards).
🤔 Frequency of model evaluations—do you run daily, weekly, or real-time checks?
✅ Post-deployment A/B tests to validate improvements over prior versions.
8.4 Top 3 Challenges
❌ Silent Failures – LLM confabulations can slip by if no alert system is in place.
❌ Interpretability – Black-box models can hamper root-cause analysis.
❌ Scale of Logs/Telemetry – Heavy instrumentation can become overwhelming.
8.5 Key Mitigations
👉 Dedicated LLM Observability Tools that track behavior at the token or embedding level.
👉 Automated Alerting (e.g., spikes in negative feedback or unusual usage patterns).
👉 Explainability Dashboards (where possible) to interpret model outputs and identify bias.
💡Now, Let’s Recap here:
Advanced Capabilities & Safety
Block 9: Model Safety & Responsible AI
Users rightly expect AI to handle data responsibly. This covers reliability, fairness, bias, and content moderation.
9.1 What Happens Here
Stress Testing for harmful or biased outputs.
Guardrail Implementation (policy filters, disclaimers, approvals) for sensitive topics or high-stakes decisions.
Red-Teaming to probe worst-case scenarios or malicious usage.
9.1.1 Lets understand the terms
Think of AI safety like child-proofing a home - you need to protect both the AI and its users from potential harm.
Reliability
Like a car's safety features
Ensures AI performs consistently
Prevents unexpected behaviors
Fairness & Bias
Think of it like a biased referee in sports:
An AI recommending jobs might favor certain groups
A loan approval system might discriminate unintentionally
Must ensure equal treatment across all user groups
Stress Testing
Like crash-testing a car
Push AI to its limits
Find breaking points before users do
Guardrails
Think of them as safety barriers:
Content filters (block inappropriate responses)
Warning systems (flag sensitive topics)
Human oversight for critical decisions
Red-Teaming
Like ethical hackers testing security:
Deliberately try to make AI fail
Identify vulnerabilities early
Plan defensive measures
Start with safety by design - it's easier than fixing problems later. Think of it as building trust with your users from day one.
9.2 What PMs Need to Know
✅ Legal & Ethical Guidelines: Integrate from the very start, not as an afterthought.
✅ Moderation & Content Policies: Understand how your AI might produce or facilitate disallowed content.
✅ Transparent Communication: If using user data, outline privacy terms or disclaimers clearly.
9.3 Strategic Decisions
👉 Decide if you need a specialized ethics board or compliance partnership.
👉 Weigh open-sourcing parts of your model or data pipelines for community scrutiny.
👉 Plan resources to tackle quickly-evolving regulations and user expectations.
9.4 Top 3 Challenges
❌ Opacity of LLMs – Hard to precisely predict model outputs or potential bias.
❌ Legal Ramifications – Non-compliance with standards like GDPR or local data laws can result in heavy penalties.
❌ Reputation Damage – Negative coverage or backlash if harmful outputs are discovered.
9.5 Key Mitigations
👉 Document Known Risks and share mitigation steps internally and externally when appropriate.
👉 Policy Filters & Fallback instructions (e.g., “Refuse to answer if…”).
👉 Ongoing Training for your team on responsible AI best practices.
💡Now, Let’s Recap here:
10. Agent Architectures & Tooling
Agents represent a step beyond classic AI pipelines. They exhibit memory, planning, autonomy, and can chain tasks or call external APIs.
10.1 What Happens Here
Agents parse user queries, break them into subtasks, and plan logical steps (sometimes self-updating prompts).
Potential to integrate with external services: real-time data, 3rd-party APIs, business logic, etc.
10.1.1 Lets understand this little better
Agents represent a significant evolution in AI capabilities, moving beyond traditional pipelines to exhibit memory, planning, and autonomy. They can perform complex tasks, interact with users, and integrate with external services.
Key Components of Modern Agents
1. Memory
Short-Term Memory: Retains context for immediate tasks, allowing agents to understand ongoing conversations or processes.
Long-Term Memory: Utilizes vector databases to store historical knowledge, enabling agents to learn from past interactions and improve over time.
2. Planning
Agents can break down user queries into subtasks and develop logical steps to achieve goals.
Advanced planning methods include:
Think-Act-Observe Loops: Iterative cycles that allow agents to refine their actions based on outcomes.
Upfront Planning: Anticipating potential actions to avoid redundancy.
3. Action
Execution of planned tasks can involve:
Interacting with users through natural language.
Calling external APIs for real-time data or business logic.
Performing automated tasks across various platforms.
10.1.2 Integration Capabilities
Agents can seamlessly connect with:
Real-Time Data Sources: Accessing up-to-date information enhances decision-making.
Third-Party APIs: Extending functionality by integrating external services.
Business Logic: Implementing specific organizational rules in task execution.
10.1.3 Multi-Agent Systems
Collaboration among multiple agents can enhance problem-solving capabilities and lead to more accurate results.
10.1.4 Tool Usage Evolution
Agents are increasingly equipped to access and utilize real-time data, improving responsiveness and relevance in their actions.
10.2 What PMs Need to Know
✅ Autonomy Levels: Determine the degree of independence an agent has.
Some agents may require human approval before executing critical actions, while others operate autonomously within defined parameters.
✅ Implement Guardrails: Agents can misinterpret instructions or create endless loops if not carefully managed.
✅ Complexity Overhead: More advanced agent capabilities mean more potential for unexpected behaviors.
More complex systems may require additional monitoring and management.
10.2.1 Keep this in mind
✅ Track performance through:
Task completion accuracy
Decision-making quality
Resource utilization efficiency
User satisfaction metrics
❌ Be vigilant for:
Unexpected behaviors or errors
Resource-intensive operations that may impact performance
Security vulnerabilities arising from external integrations
10.3 Strategic Decisions
⚖️ Auto-execution vs. “Human-in-the-loop.”
👉 Incorporate cost budgets, concurrency limits, or timeouts to keep runaway tasks in check.
✅ Logging & replay capabilities to diagnose misbehaviors or user disputes.
10.4 Top 3 Challenges
❌ Infinite Loops – Agents continually re-invoke themselves or get stuck.
❌ Security Exposures – Agents calling external tools might inadvertently open vulnerabilities.
❌ Multiple Dependencies – Agents often require cross-model synergy (LLM + retrieval + transformations).
10.5 Key Mitigations
👉 Time/Cost Budgets that limit agent steps to avoid runaway tasks.
👉 Strong Logging & Audit Trails to trace “reasoning.”
👉 Clear Role Descriptions that keep the agent bound to allowed actions.
💡Now, Let’s Recap here:
Conclusion
This framework empowers PMs—regardless of their expertise level—to evaluate their unique contexts critically.
As we move forward into deeper dives on each block in future posts, remember that informed decision-making is key.
The landscape of AI is rich with possibilities; understanding these foundational elements will enable you to harness its full potential effectively.
I encourage you to reflect on what you've learned so far and consider how it applies to your own products. Let’s continue this journey together as we explore each block in detail in upcoming posts—building confidence and knowledge along the way!
Check all future posts here: Niche Skills: AI Product Management.