Code 231

AI Product Strategy: A 10-Point Framework to Build AI Products That Deliver Real Impact

Original post:

__________

Most AI product conversations start with the visible part of the iceberg:

LLMs
RAG
Agents
Fine-tuning
Vector databases
Prompting

But in real AI products, technology is only the surface.

The part that determines whether the product succeeds is usually underneath:

Do we understand the real problem?
Do users actually need this workflow?
Can we trust the data?
Are security, privacy, and compliance handled properly?
Who owns the AI outputs?
How do we measure quality?
Can the product scale without becoming too expensive?
What happens when the system drifts, fails, or gives a risky answer?

That is why AI Product Strategy matters.

It is not just about picking the newest model or adding an AI feature to the roadmap.
It is about building a product system around AI: the problem, the users, the data, the risks, the operating model, and the feedback loops.

A simple lesson I keep coming back to:

Use RAG when you need fresh knowledge, source grounding, or access to changing/internal documents.

Use fine-tuning when you need consistent task behavior, style, format, or domain-specific patterns and you have high-quality representative examples.

Use agents when the workflow requires multiple steps, tools, decisions, and planning.

And before shipping, do not only ask: "Does it work?"

Ask:

Is it useful?
Is it trusted?
Is it safe?
Is it measurable?
Is it maintainable?
Is it ready for real users?

AI is not just about models.
It is about people, process, and purpose.

Start small, learn fast and build AI products that create real value.

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Most new AWS learners think “WAF and Shield are both security tools.”

Here’s how to transform that mindset into real cloud security confidence:

STEP 1 - Know what each tool blocks
-WAF handles SQLi and bots
-Shield blocks massive DDoS attacks
-Use both for full coverage

STEP 2 - Learn where each fits
-WAF = app layer (API Gateway, ALB)
-Shield = infra layer (EC2, CloudFront)
-Think in layers, not silos

STEP 3 - Compare pricing models
-WAF = pay-per-rule
-Shield Standard = free
-Shield Advanced = premium protection

STEP 4 - Customize what matters
-WAF: Create fine-grained control with rules
-Shield: Auto-mitigation with less manual effort

STEP 5 - Use them together
-Secure APIs and web apps with WAF
-Absorb DDoS threats with Shield

Which part of your AWS security stack needs a second look?

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Something strange is happening in the world of AI.

Everyone talks about models.
But very few talk about the 𝐬𝐲𝐬𝐭𝐞𝐦 𝐛𝐞𝐡𝐢𝐧𝐝 𝐭𝐡𝐞𝐦.

The real magic of modern AI does not start with prompts.
It starts with 𝐚 𝐩𝐢𝐩𝐞𝐥𝐢𝐧𝐞.

And when that pipeline is built right, intelligence becomes scalable.

Here is what an 𝐄𝐧𝐝 𝐭𝐨 𝐄𝐧𝐝 𝐑𝐀𝐆 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰 actually looks like.

→ 𝐈𝐧𝐠𝐞𝐬𝐭 𝐚𝐧𝐝 𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐃𝐚𝐭𝐚
• Collect raw data from multiple sources
• Clean and normalize the data

→ 𝐒𝐩𝐥𝐢𝐭 𝐈𝐧𝐭𝐨 𝐂𝐡𝐮𝐧𝐤𝐬
• Break large documents into smaller pieces
• Make them easier for models to process

→ 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬
• Convert text into vector representations
• Examples
• llama-text-embed-v2
• text-embedding-3-large
• e5-large-v2

→ 𝐒𝐭𝐨𝐫𝐞 𝐢𝐧 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐁 𝐚𝐧𝐝 𝐈𝐧𝐝𝐞𝐱
• Store embeddings for fast similarity search
• Vector DBs
• Document DBs
• Knowledge Graphs

→ 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐞
• Fetch the most relevant context from stored data
• Improve relevance with rerankers
• bge-reranker-v2-m3
• cohere-rerank-3.5

→ 𝐒𝐞𝐥𝐞𝐜𝐭 𝐋𝐋𝐌𝐬 𝐟𝐨𝐫 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧
• Choose the model that generates the final answer

→ 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐞 𝐭𝐡𝐞 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞
• Connect every step into a structured workflow

→ 𝐀𝐝𝐝 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲
• Monitor model behavior and system performance
• Track LLM invocations using synthetic and human inputs

→ 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 𝐚𝐧𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧
• Unit tests
• Human review
• Model based evaluation
• A B tests

→ 𝐈𝐦𝐩𝐫𝐨𝐯𝐞 𝐭𝐡𝐞 𝐒𝐲𝐬𝐭𝐞𝐦
• Prompt engineering
• Fine tune curated data
• Evaluation and curation loops

→ 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐚𝐧𝐝 𝐕𝐞𝐫𝐬𝐢𝐨𝐧 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠
• Gradually automate the pipeline
• Version models, prompts, and data together

This is how 𝐀𝐈 𝐒𝐞𝐚𝐫𝐜𝐡 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐚𝐫𝐞 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐛𝐮𝐢𝐥𝐭.

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If AI still feels confusing
it is not your fault.
The problem is scattered tools and no clear roadmap.
If you want a structured path from basics to production AI
👉 Comment/AI

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Agentic AI in 2026 = The biggest upgrade to how software is built, deployed, and operated.

And the people who understand how agents actually work will lead the next wave of tech innovation.

Most professionals still see Agentic AI as “better prompting.”
In reality, it’s a full ecosystem - reasoning engines, memory systems, tool ex*****on, multi-agent workflows, safety layers, and operational tooling.

Here’s a simple breakdown of what you need to learn to stay ahead:

🔹 Agentic AI Basics
Understand what agents are, how they differ from standard LLMs, and why autonomy, reasoning, and tool-use separate them from traditional automation.

🔹 Core Agent Components
Agents rely on four pillars:
• Intent understanding
• Reasoning & planning
• Memory systems
• Tool use & API ex*****on
These functions decide how an agent interprets tasks and takes action.

🔹 Agent Frameworks & Tools
Platforms like OpenAI Agents, LangGraph, CrewAI, AutoGen, LlamaIndex, and HuggingFace Agents help you build real production-ready agents.

🔹 Key Agentic Capabilities
Planning, multi-step reasoning, scheduling, RAG, and multi-modal retrieval - the abilities that turn agents into problem-solvers instead of text generators.

🔹 Ex*****on & Multi-Agent Collaboration
How agents delegate tasks, communicate, call APIs, run workflows, and coordinate with other agents to complete complex goals.

🔹 Safety & Governance
Guardrails, output validation, ethical constraints, security layers, and data-privacy systems - essential for trustworthy AI.

🔹 AgentOps (Agentic DevOps)
Versioning, CI/CD for AI pipelines, monitoring, observability, model registries, dataset tracking, infra-as-code - everything needed to operate agents reliably in production.

Agentic AI isn’t optional anymore.
If you want to stay relevant, you need to understand how agents think, act, plan, and collaborate.

Which part are you planning to learn first - reasoning, memory, or tool ex*****on?

What do AI Engineers do?. There are 2 types of AI Engineers.

Most people don't know this. Hiring managers use the title interchangeably.

Here's the breakdown:

🔴 AI Engineer who Builds WITH LLMs Designs and ships AI-powered applications.
→ Prompt engineering
→ RAG and vector search
→ Tools and integrations (MCP, APIs)
→ System design
→ Deployment and monitoring

Their job: Build AI products users actually use. Ship features. Solve problems. Make it work in production.

🔵 AI Engineer who Builds LLMs Trains models and builds the foundation of AI itself.
→ Model architecture
→ Pretraining and fine-tuning
→ Optimization and math
→ Evaluation and benchmarking
→ Research and innovation

Their job: Push the limits of model performance. Advance the frontier. Work on math, algorithms, and data at scale.

Same title. Completely different skillsets. Completely different interviews.
The confusion costs companies bad hires.

It costs candidates wasted prep time.
One needs a strong engineering background and system design skills.

The other needs deep ML research and math.

Know which one you are before your next job application.

Know which one you need before your next hire.

Which side are you on? 👇

Follow us for more on AI 🤖 × Cloud ☁️ × Capital Markets 📊

I spent 6 months collecting every
Claude trick I know.

Then I put all 100 on one page.

Here's what's inside:

𝟭. Setup (Tips 1-10)
↳ Pick Opus for hard tasks,
Sonnet for speed
↳ Turn on memory, artifacts,
web search
↳ Link Gmail, Drive, Slack, Notion
↳ Keyboard shortcuts save more time
than people realize

𝟮. Prompting (Tips 11-20)
↳ Specific beats vague every time
↳ XML tags changed my output quality
overnight
↳ Tell Claude what NOT to do

𝟯. Memory and Context (Tips 21-30)
↳ Edit and delete memory anytime
↳ Pin a style guide to a project
and never re-explain it
↳ Incognito starts fresh

This is where it gets interesting.

𝟰. Claude Code (Tips 31-40)
↳ curl -fsSL | sh installs it
↳ Plan mode thinks before coding
↳ Pipe git diff for instant reviews
↳ @ mentions pull in specific files

𝟱. Commands (Tips 41-50)
↳ /resume recovers crashed sessions
↳ /compact clears bloated context
↳ /model switches without restarting
↳ /buddy. Just try it.

𝟲. CLAUDE. md (Tips 51-60)
↳ Loads automatically every session
↳ Coding standards go here once
↳ Custom commands live in
.claude/commands/

𝟳. Artifacts (Tips 61-70)
↳ Full React apps inside Claude
↳ Live dashboards with charts
↳ Export to .md, .html, .docx

𝟴. MCP and Connectors (Tips 71-80)
↳ 200+ connectors exist
↳ One click to set up
↳ Free and Pro both get access

𝟵. Cowork and Agents (Tips 81-90)
↳ Persistent memory across sessions
↳ Multi-agent orchestration
↳ Routines run while I sleep

𝟭𝟬. Power User (Tips 91-100)
↳ Web search + connectors + artifacts
in a single prompt
↳ Sub-agents handle delegation
↳ /context before any major move

I could have split this into 10 posts.

But the whole point was one page,
zero fluff, no hunting across
10 different carousels.

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🚨 Most people think using ChatGPT, adding RAG, or automating workflows means they’ve built “Agentic AI.”

They haven’t.

Because Agentic AI is not just about generating responses.
It’s about autonomous reasoning, planning, coordination, memory, and ex*****on. 🧠⚡

This diagram explains one of the biggest misconceptions in modern AI architecture 👇🏻

❌ LLM Chatbots are NOT Agentic AI
❌ RPA + LLM automation is NOT Agentic AI
❌ Basic RAG pipelines are NOT Agentic AI

These systems are intelligent…
but they are still mostly reactive.

Here’s the real difference 👇🏻

1️⃣ LLM Chatbots
Traditional LLM systems work in a simple flow:

User Prompt → LLM → Response

They are excellent at:
• conversation
• summarization
• content generation
• Q&A workflows

But they typically lack:

❌ long-term memory
❌ autonomous planning
❌ self-correction
❌ multi-step ex*****on
❌ environment interaction

They respond intelligently…
but they don’t independently pursue goals.

2️⃣ RPA + LLM Automation
This layer adds automation on top of AI. ⚙️

Now systems can trigger workflows, APIs, or predefined tools.

But most of these automations are still:

• rule-based
• deterministic
• workflow constrained
• human-directed

They automate tasks…
but they don’t truly reason through objectives dynamically.

3️⃣ RAG Pipelines
RAG dramatically improves AI by giving models access to external knowledge. 📚

This enables:

✅ document retrieval
✅ vector search
✅ enterprise knowledge access

And this is where Agentic AI begins. 🚀

4️⃣ What Actually Makes a System “Agentic”?

A true Agentic AI system combines:

🧠 Memory
→ persistent context, episodic learning, long-term state management

📋 Planning
→ goal decomposition, reasoning chains, decision trees, ex*****on strategies

🛠️ Tool Usage
→ APIs, browsers, IDEs, databases, external software systems

🔄 Feedback Loops
→ reflection, evaluation, self-correction, iterative improvement

🤝 Multi-Agent Collaboration
→ specialized agents coordinating tasks together

🌍 Environment Interaction
→ dynamically responding to changing conditions in real time

But autonomous ecosystems capable of reasoning, adapting, collaborating, and executing objectives end-to-end. ⚡

The shift happening right now is massive:

🔹 From chatbots → AI workers
🔹 From prompts → autonomous workflows
🔹 From retrieval → reasoning + ex*****on
🔹 From tools → orchestrated intelligence

Agentic AI is not just another AI buzzword.

It’s the evolution from “generating answers” → to “achieving outcomes.” 🎯

🧩 Enabling intelligent systems, AI-driven workflows & scalable architectures with

⚡ Engineering solutions built for real-world impact.

Zero Trust isn't "trust nobody."
It's "verify everybody - every time."

That one reframe is what gets people unstuck on the concept. It's not paranoia. It's the only realistic security model when your users, data, and apps live everywhere.

Here's Zero Trust in one view 👇

• Verify Explicitly - no implicit trust; identity + context every request
• Least Privilege - minimum permissions, just-in-time
• Assume Breach - design to contain, not just prevent

The flow: User → AuthN/AuthZ → Context (device, location, time) → Access Policies → Protected Resource, with continuous monitoring and threat intel re-evaluating in real time.

The building blocks: IAM, device security, micro-segmentation, continuous monitoring.
In practice: MFA, segmented workloads, just-in-time access.

The payoff: smaller blast radius, granular access control, full visibility.

Save this for your next security review.

Which principle is hardest to implement? 👇

9 database types explained in one sentence:

1) 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹
↳ Stores structured data in tables with predefined schemas & SQL queries.

2) 𝗞𝗲𝘆-𝗩𝗮𝗹𝘂𝗲
↳ Stores simple key-value pairs for ultra-fast lookups & caching.

3) 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁
↳ Stores data as JSON-like documents with flexible, nested structures.

4) 𝗪𝗶𝗱𝗲-𝗖𝗼𝗹𝘂𝗺𝗻
↳ Stores data in flexible column families for large-scale distributed workloads.

5) 𝗧𝗶𝗺𝗲-𝗦𝗲𝗿𝗶𝗲𝘀
↳ Stores time-stamped data for real-time metrics, logs, events, & telemetry.

6) 𝗚𝗿𝗮𝗽𝗵
↳ Stores relationships between entities to query connected data efficiently.

7) 𝗩𝗲𝗰𝘁𝗼𝗿
↳ Stores embeddings to enable similarity search & AI-powered retrieval.

8) 𝗖𝗼𝗹𝘂𝗺𝗻𝗮𝗿
↳ Stores data by columns instead of rows to optimize analytical queries.

9) 𝗦𝗲𝗮𝗿𝗰𝗵
↳ Stores indexed text and structured data to enable fast full-text and relevance-based queries.

Most modern systems use several of these together.

As systems become more real-time and AI-driven, the need for time-series infrastructure has grown significantly.

I like using TimescaleDB by Tiger Data because it keeps the simplicity of Postgres while making it much easier to work with large volumes of time-series and real-time data.

Try Tiger Data free with my link below. You'll get a $1,000 30-day credit, no credit card required. It takes just a few minutes to get started, and you can use the credit to build and experiment with whatever you want (new accounts only).

Try it here (for free) → https://lnkd.in/gYDyg5Tn

What else would you add?

——

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SQL expertise has 5 levels.

- Level 1: Basics

SELECT, FROM, WHERE, GROUP BY, HAVING, LIMIT.

These are the SQL keywords you use in every query.

With them, you can:
→ Filter data
→ Sort results
→ Build basic reports
→ Aggregate a bunch of records

- Level 2: Joins

This is where SQL starts becoming powerful.

Most of the time, you’ll use:

→ INNER JOIN
→ LEFT JOIN

And much less often:

→ RIGHT JOIN
→ CROSS JOIN
→ FULL OUTER JOIN

If you understand joins well, you understand SQL well.

- Level 3: Window Functions

This is where SQL becomes a serious skill.

You need to understand:

→ PARTITION BY: split the window
→ ORDER BY: order the window

And the difference between:

→ RANK
→ DENSE_RANK
→ ROW_NUMBER

Window functions are one of the biggest jumps from beginner to intermediate SQL.

- Level 4: The Architect

You can’t just query the data.
You also need to understand how the structure is built.

That means knowing DDL:

→ CREATE
→ ALTER
→ DROP

And also understanding transactions:

→ COMMIT
→ ROLLBACK

Because SQL is not only about reading data.
It’s also about designing and managing it correctly.

- Level 5: The Optimizer

This is the superior level.
You don’t just write SQL.

You understand:

→ Indexes
→ Partitions
→ Table scans
→ Query performance
→ How the database actually executes your query

2 queries can return the same result.

But one can run in 2 seconds.

And the other can destroy your warehouse.

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