🧠 Tutorial: “Master SQL Using Perplexity AI — From Beginner to Pro” by Ajay Architect

⏱ Tutorial Duration: 15–30 minutes approximate 

💡 Goal:  Teach how to use AI tools like Perplexity, ChatGPT or Gemini to write, optimize, and analyze SQL queries — without manual coding.

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🔹 1. Understanding the Concept: SQL + AI

Traditional SQL writing involves:

SELECT customer_name, SUM(amount)   FROM orders  WHERE order_date >= '2025-01-01'  GROUP BY customer_name  ORDER BY SUM(amount) DESC;  

With AI tools, you can simply say:

🗣️ "Show me the top 10 customers by total purchase value since January 2025."

AI converts this natural language into the above SQL automatically.

✅ AI Advantage: You save hours of manual query building, debugging, and syntax lookup.

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🔹 2. How to Use AI for SQL Generation

Example 1: Basic Data Extraction

🗣️ Prompt to AI:

Write an SQL query to list all employees from the "Sales" department earning more than ₹70,000 per month.

🧩 AI Output:

SELECT name, department, salary  FROM employees  WHERE department = 'Sales'    AND salary > 70000;  

You can even ask AI to:

Convert this query for PostgreSQL / MySQL / BigQuery.

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🔹 3. AI for Database Design

AI can also design database schemas for you.

🗣️ Prompt:

Design a database for an e-commerce company with tables for customers, products, and orders.

🧩 AI Output:

CREATE TABLE customers (      customer_id INT PRIMARY KEY,      name VARCHAR(100),      email VARCHAR(100),      city VARCHAR(50)  );    CREATE TABLE products (      product_id INT PRIMARY KEY,      name VARCHAR(100),      category VARCHAR(50),      price DECIMAL(10,2)  );    CREATE TABLE orders (      order_id INT PRIMARY KEY,      customer_id INT,      product_id INT,      order_date DATE,      quantity INT,      FOREIGN KEY (customer_id) REFERENCES customers(customer_id),      FOREIGN KEY (product_id) REFERENCES products(product_id)  );  

✅ AI saves hours by creating entire schema structures instantly.

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🔹 4. Query Optimization Using AI

Example:

🗣️ Prompt:

Optimize this query for faster performance.

SELECT * FROM orders WHERE customer_id IN (SELECT customer_id FROM customers WHERE city = 'Pune');  

🧩 AI Suggestion:

SELECT o.*  FROM orders o  JOIN customers c ON o.customer_id = c.customer_id  WHERE c.city = 'Pune';  

✅ AI explains: "JOINs are generally faster than subqueries."

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🔹 5. Debug SQL Errors Using AI

🗣️ Prompt:

Fix this query:

SELECT name, salary FROM employee WHERE salary => 50000;  

🧩 AI Correction:

SELECT name, salary FROM employee WHERE salary >= 50000;  

✅ AI instantly spots syntax and logic errors.

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🔹 6. AI-Powered Data Insights

Once you've data in your database, ask:

🗣️ "Which city has the highest average sales in 2025?"

🧩 AI Output:

SELECT city, AVG(amount) AS avg_sales  FROM orders  WHERE YEAR(order_date) = 2025  GROUP BY city  ORDER BY avg_sales DESC  LIMIT 1;  

✅ Use this to automate reporting and dashboards.

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🔹 7. AI for SQL Interview Prep

AI can simulate interview questions:

🗣️ "Ask me 5 SQL interview questions with answers for a Data Analyst role."

🧩 Example Output:

1. What is a Primary Key?
   A column (or group of columns) that uniquely identifies each record in a table.

   CREATE TABLE students (id INT PRIMARY KEY, name VARCHAR(50));  

2. Difference between INNER JOIN and LEFT JOIN

   • INNER JOIN returns matching rows.
   • LEFT JOIN returns all rows from the left table, plus matched rows from the right.

…and so on.

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🔹 8. Building a Full AI + SQL Project

🧩 Example: Sales Dashboard

AI can generate:

1. SQL to extract data from orders
2. Python code (using Pandas + SQLAlchemy)
3. Chart commands for visualization

🗣️ Prompt:

Build a small dashboard showing monthly revenue trends using SQL and Python.

✅ AI creates both SQL and Python scripts for you — saving 5+ hours of manual coding.

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🔹 9. Tools You Can Use

• Perplexity/ ChatGPT / Gemini / Claude — for SQL query generation and debugging
• DigitalTechnologyArchitecture Blog — for live guided sessions
• SQLBolt / Mode Analytics / DB Fiddle — to practice queries online
• LeetCode SQL — to test optimization skills

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🔹 10. Certification Path

I conduct workshops and at the end of such a workshop, you'll typically:

• Complete hands-on exercises
• Submit AI-generated SQL assignments
• Get an industry-recognized certificate

🎓 Example:

"Certified SQL Using AI Professional – AI for SQL Developers"

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✅ Summary - HOw SQL developer can save time using AI?

Task	Traditional Time	With AI
Write query	30 mins	1–2 mins
Design schema	2 hrs	5 mins
Optimize SQL	1 hr	Instant
Debug errors	45 mins	Seconds
Interview prep	3 hrs	15 mins

💡 Total time saved: 5 hours → 15 minutes

Feel free to share the tutorial with your friends and visit agin for more quick tutorial on AI 

For workshop write to projectncharge@yahoo.com or smartmobileideas@gmail.com 

Enjoy! - Ajay Architect 

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Enhancing Traditional Architecture for AI: A Guide by an Enterprise IT Architect

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Enhancing Traditional Architecture for AI: A Guide by an Enterprise IT Architect

Introduction

With decades shaping large-scale systems at Google and Microsoft, I’ve seen AI go from experimental to foundational. For many organizations running stable 4-tier or SOA architectures, the key question is: How do we integrate AI safely, transparently, and sustainably? In this guide, I outline a refined architectural approach for embedding AI — not as an afterthought, but as a first-class, governed capability.

1. Ground What’s Proven: The 4-Tier Architecture

The familiar pattern holds:

• Client/UI – Web, mobile, or desktop.

• Presentation/API – Gateways, controllers, entry points.

• Application/Services – Business logic, orchestration.

• Data Tier – Databases, caches, storage.

This modular structure remains solid. The challenge: augment, not disrupt.

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2. Why AI Works Best as a Layered Service

Based on enterprise best practices:

• SOA compatibility: AI fits naturally as a loosely coupled, contract-based service .

• Modular AI Services: Package models as APIs behind SLAs, support versioning, autoscale independently — just like Google/Microsoft deploy theirs 

• Semantic Enrichment: Introduce a “Knowledge Layer” (e.g., RAG or ontology-backed context) that grounds AI across data silos and documents 

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3. Diagram Inspiration

 

Top Image — Semantic Layer Architecture

From Enterprise Knowledge: illustrates how businesses embed AI understanding into structured datasets (taxonomies, ontologies, knowledge graphs), enabling richer semantic context across services.

Bottom Image — Layered Enterprise AI Blueprint

From Infosys: presents a multi-layer AI reference architecture — from infrastructure and engineering processes to governance — that bridges AI and traditional IT infrastructure.

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4. Core Architectural Patterns

4a. Treat AI Models as First-Class Services

• Wrap inference models in versioned microservices (REST/gRPC).

• Use tools like KServe or Seldon for model serving, autoscaling, and canary deploys (DEV Community).

4b. Data-Centric, Event-Driven Pipelines

• Use streaming platforms (e.g., Kafka) to feed models real-time events.

• Store features in feature stores (e.g., Feast, Tecton) for consistency across training and inference (DEV Community).

4c. Semantic / Knowledge Layer

• Integrate knowledge graphs or ontologies to enrich AI context.

• Empower grounded AI responses using structured business knowledge (LinkedIn).

4d. SOA + AI Synergy with Governance

• Use principles like service composability and loose coupling to manage AI services (Wikipedia).

• Embed observability, privacy, and lifecycle tracking.

4e. Evolving Toward Agentic AI

• McKinsey highlights the rise of “agentic meshes”: autonomous AI agents that operate cooperatively and continuously, beyond single-model responses (TechRadar).

• Architecting for them means enabling real-time data, shared memory, auditability, and control.

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5. Enterprise Deployment Blueprint

Component	Enterprise Enhancement
AI Infrastructure	Cloud/on-prem orchestration, GPU/autoscaling, unified model deployment
Model Services	Containerized, versioned, API-first deployments (KServe, CI/CD integration)
Data Pipelines	Event ingestion, feature stores, feedback loops for model retraining
Knowledge Layer	Ontologies, knowledge graphs, taxonomy services for context grounding
SOA Governance	Contracts, policy enforcement, audit logs, reuse policies
Agentic Readiness	Support event-driven services, real-time APIs, memory, and orchestration layers

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6. Deployment Roadmap

1. Pilot – Weeks 0–4: Select a high-impact AI use case (e.g., intelligent search or auto summarization). Deploy standalone service behind API gateway, with observability and SLAs.

2. Integration – Weeks 5–8: Add feature pipelines, connect knowledge layer, integrate with SOA services.

3. Governance – Weeks 9–12: Build monitoring dashboards (latency, cost, bias), establish model registry, and audit logs.

4. Agentic Transition – Months 3–6: Lay groundwork for autonomous agents, real-time event triggers, and shared memory patterns.

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Conclusion

As an architect with Google and Microsoft DNA, I've seen firsthand that true enterprise AI isn’t about smarter models — it’s about smarter systems. Begin with the strong foundation you already have, layer AI services thoughtfully, anchor them with data, semantics, and governance — and then, as maturity grows, evolve toward agentic, autonomous orchestration.

This is not just an AI feature, but a strategic architectural commitment.

Let me know if you’d like me to craft fully polished diagrams (SVG-style) or tailor this for a whitepaper or executive summary.

How AI Can Assist You in Your Legal Case

Artificial Intelligence (AI) can be a helpful assistant in preparing for legal matters. By scanning through lakhs of cases across India and the world, AI can highlight judgments that support your side, point out possible weaknesses in your opponent's case, and suggest strategic directions.

Below are some simple ways to use AI with example prompts:

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Step 1: Upload Your Case Document

If you have your case details in a PDF file, you can upload it into an AI tool (like ChatGPT). Once uploaded, you can ask AI to:

Example Prompt 1:
"I am uploading my case PDF. Please summarize the key facts, main issues, and what relief is being sought."

Example Prompt 2:
"Highlight the timeline of events in this case PDF in a simple table format."

Example Prompt 3:
"Please identify the main legal sections and acts referred to in this case."

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Step 2: Find Supporting Cases

AI can search legal databases and identify cases that are similar to yours.

Example Prompt 4:
"Based on this uploaded case PDF, please list 5 Indian Supreme Court judgments that support my arguments. Provide case names and short summaries."

Example Prompt 5:
"Please find global case references (UK, US, etc.) that may strengthen my side of the argument."

Example Prompt 6:
"List any High Court judgments from the last 10 years that are relevant to my case facts."

Example Prompt 7:
"Compare my case PDF with [insert case name] and tell me how they are similar or different."

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Step 3: Identify Weak Points in Opponent's Case

AI can act like a neutral checker and point out where your opponent might attack.

Example Prompt 8:
"Please analyze this case PDF and highlight potential weak points that my opponent's lawyer may raise."

Example Prompt 9:
"List the possible counterarguments that the other side may use against my claims."

Example Prompt 10:
"Check if any legal precedents exist that could weaken my case position."

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Step 4: Strategy Recommendations

Once strengths and weaknesses are identified, you can ask AI for possible strategies.

Example Prompt 11:
"Given the facts of my case and the legal precedents, suggest 3 strategic approaches that my lawyer can use in court."

Example Prompt 12:
"If the opponent argues XYZ, suggest counterarguments supported by legal precedents."

Example Prompt 13:
"Please create a list of questions my lawyer can ask during cross-examination to strengthen my position."

Example Prompt 14:
"Draft a sample written submission based on the uploaded case facts and supporting case law."

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Step 5: Simplify Legal Language

Legal documents are often complex. AI can explain them in simple terms.

Example Prompt 15:
"Please explain the uploaded case PDF in simple language as if explaining to a 10-year-old."

Example Prompt 16:
"Summarize this legal section (IPC/Act) in plain English/Marathi/Hindi."

Example Prompt 17:
"Give me a bullet-point explanation of this judgment for a non-lawyer."

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Step 6: Practical Preparation

You can also use AI for practical legal preparation.

Example Prompt 18:
"Create a checklist of documents and evidence I should collect based on my case PDF."

Example Prompt 19:
"Suggest a timeline for next steps in my legal process (filing, hearings, appeals)."

Example Prompt 20:
"Draft possible questions I should ask my lawyer when we meet to discuss this case."

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Important Note

AI is not a substitute for a qualified lawyer. It is a research and support tool to help you prepare better, understand your case more clearly, and explore different strategies.

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In Short:

• Upload your case PDF.
• Ask AI to summarize, find similar cases, and identify weaknesses.
• Use AI to test arguments, draft strategies, and simplify legal terms.
• Always discuss the final plan with your lawyer.

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This way, AI becomes your legal assistant – working tirelessly to scan lakhs of cases and helping you prepare smarter.

प्रॉम्प्ट इंजिनिअरिंग: सविस्तर मार्गदर्शक (उदाहरणांसह)

मी हा लेख कृत्रिम बुद्धिमत्ता (Artificial Intelligence) आणि तिचा वापर कसा करावा याबद्दल लिहिला आहे, जेणेकरून इंग्रजीत सहज बोलू न शकणाऱ्या आपल्या मराठी बांधवांना सोप्या भाषेत AI शिकता येईल. भविष्यात तुम्हाला AI विषयक अजून पोस्ट्स मराठीत पाहायला मिळतील. कृपया हा लेख आपल्या मराठी मित्र, विद्यार्थी आणि ज्येष्ठ नागरिकांपर्यंत पोहोचवा, जेणेकरून त्यांनाही AI शिकता येईल.  जनरेटिव्ह (Generative) AI च्या काळात, प्रॉम्प्ट इंजिनिअरिंग हे कौशल्य AI शी प्रभावीपणे संवाद साधण्यासाठी सर्वात आवश्यक ठरले आहे.

👉 इंग्रजी आवृत्तीसाठी लिंक:  Read Prompt Engineering in English

जनरेटिव्ह AI म्हणजे काय?

• AI (कृत्रिम बुद्धिमत्ता) म्हणजे संगणकाला माणसासारखं विचार करायला, शिकायला आणि निर्णय घ्यायला शिकवणं.

• Generative AI म्हणजे अशी कृत्रिम बुद्धिमत्ता जी स्वतःहून नवीन गोष्टी तयार करू शकते. 

सोपं उदाहरण

जर तुम्ही एखाद्या मित्राला सांगितलंत की, “मला सिंहाचं चित्र काढून दाखव.”  तो मित्र स्वतः कल्पना करून सिंहाचं चित्र काढून देईल. Generative AI पण तसंच आहे — तुम्ही त्याला prompt (म्हणजे सूचना/मागणी) देता, आणि ती AI नवीन मजकूर, चित्र किंवा संगीत तयार करून देते.

चला आता Prompt Engineering म्हणजे काय, प्रॉम्प्ट कसा लिहायचा आणि मग आजपासूनच ChatGPT बरोबर त्याचा वापर कसा सुरू करायचा ते पाहूया!  १० वर्षांच्या मुलापासून ते ७९ वर्षांच्या ज्येष्ठांपर्यंत प्रत्येकजण आपल्या मोबाईलवरून हे सहज वापरू शकतो.
तेवढं हे सोपं आहे!

नवशिक्यांसाठी ३ लोकप्रिय AI साधने म्हणजे –

1. ChatGPT (OpenAI चे) - Open chatgpt

2. Gemini (Google चे)

3. Claude (Anthropic चे)

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हे कृत्रिम बुद्धिमत्ता (AI) नेमके कसे काम करते?

1. तुम्हाला माहीत आहेच की संगणकावर सॉफ्टवेअर चालते, तो इंटरनेटवर शोध घेऊ शकतो आणि डेटा साठवू शकतो.

2. १०,००० संगणक १ संगणकापेक्षा कितीतरी पट वेगाने इंटरनेटवर शोध घेऊ शकतात व माहिती साठवू शकतात.

3. जर मी संगणकाला "डॉल्फिन" किंवा "कॉफी" बद्दल माहिती शोधायला सांगितले, तर तो सगळी माहिती साठवतो आणि जेव्हा मी प्रश्न विचारतो, तेव्हा काही सेकंदांत उत्तर देतो.

4. AI असंच काम करतं – लाखो संगणक विशिष्ट "शब्दांबद्दल" माहिती शोधतात व साठवतात आणि आपण प्रश्न विचारल्यावर ते सेकंदात उत्तर देतात.

5. प्रॉम्प्ट इंजिनिअरिंग म्हणजे संगणकाला असा आदेश (कमांड) लिहिणे ज्यामुळे त्याला नेमके काय हवे आहे ते समजेल आणि तो सर्वोत्तम उत्तर देईल.

6. जर मला १० वर्षांच्या मुलाला "कॉफी कशी बनवतात" हे समजावून सांगायचे असेल, तर संगणकाला तशी सूचना द्यावी लागेल, ज्यामुळे त्याचे उत्तर त्या मुलाला सहज समजेल.

7. पण जर मला ३० वर्षांच्या व्यक्तीला "घरी ब्रू कॉफी कशी बनवतात" हे विचारायचे असेल, तर मी वेगळ्या प्रकारे प्रश्न विचारला पाहिजे.

8. जितका जास्त संदर्भ (Context) तुम्ही द्याल, तितकं AI कडून मिळणारं उत्तर चांगलं येईल.

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वाचन सुरू करण्यापूर्वी काही प्रश्न

• साध्या माणसाला AI साधनांशी बोलून चांगले उत्तर मिळू शकेल का?

• बायको, आई, विद्यार्थी, वकील, डॉक्टर, शेफ यांच्या आयुष्यात कृत्रिम बुद्धिमत्तेचा काही उपयोग आहे का?

• मी आजपासून AI वापरायला सुरूवात करू शकतो का?

• मी ७९ वर्षांचा आहे – तरी AI मला मदत करू शकेल का?

वरील सर्व प्रश्नांची उत्तरे = होय ✅

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प्रॉम्प्ट इंजिनिअरिंग म्हणजे काय?

प्रॉम्प्ट इंजिनिअरिंग म्हणजे AI ला अशी इनपुट लिहिण्याची प्रक्रिया ज्यामुळे अपेक्षित, उपयुक्त व अचूक उत्तर मिळते. ChatGPT सारखी मॉडेल्स प्रचंड डेटासेटमधील पॅटर्न्सवर आधारित उत्तर तयार करतात. म्हणून आपण प्रश्न कसा विचारतो, यावर उत्तर बऱ्याच प्रमाणात अवलंबून असते.

मुळात, प्रॉम्प्ट इंजिनिअरिंग म्हणजे:

• ChatGPT किंवा Gemini इनपुट कसा समजतात हे जाणून घेणे.

• मॉडेलच्या वर्तनाला दिशा देणारे प्रॉम्प्ट्स तयार करणे.

• परिणाम सुधारण्यासाठी प्रॉम्प्ट्समध्ये सतत सुधारणा करणे.

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प्रॉम्प्ट इंजिनिअरिंग का महत्वाचे आहे?

AI मॉडेल्स शक्तिशाली असतात, पण ते विचार वाचू शकत नाहीत. ते फक्त दिलेल्या मजकुरावर अवलंबून असतात.
शब्दरचना, टोन, तपशील, रचना यामधील छोटासा फरकही परिणाम बदलू शकतो.

चांगल्या प्रॉम्प्ट इंजिनिअरिंगचे फायदे:

• अधिक अचूक व संबंधित उत्तरे

• चुकीची किंवा काल्पनिक माहिती कमी होणे

• वेळेची बचत

• शैक्षणिक, व्यावसायिक किंवा सर्जनशील उद्दिष्टांशी अधिक सुसंगत उत्तरे

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प्रॉम्प्ट इंजिनिअरिंगची मूलभूत तत्त्वे

1. स्पष्टता (Clarity)

   • प्रॉम्प्ट जितका स्पष्ट, उत्तर तितके स्पष्ट.

   • गोंधळ टाळा.

2. विशिष्टता (Specificity)

   • प्रॉम्प्ट जितका नेमका, उत्तर तितकं चांगलं.

   • फॉरमॅट, टोन, लांबी किंवा दृष्टिकोन लिहा.

3. संदर्भ (Contextualization)

   • पार्श्वभूमी दिल्यास अधिक योग्य उत्तर मिळते.

4. सूचनात्मक भाषा (Instructional Language)

   • "List", "Summarize", "Compare" सारखी क्रियापदे वापरा.

5. पुनरावृत्ती (Iteration)

   • उत्तरे तपासा व आवश्यकतेनुसार प्रश्न पुन्हा लिहा.

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प्रॉम्प्ट्सचे प्रकार

1. वर्णनात्मक (Descriptive)

   • "मंगळ ग्रहाचे वातावरण वर्णन करा."

   • "सप्टेंबर २०२६ मध्ये हवाईचे हवामान कसे असेल?"

2. सूचनात्मक (Instructional)

   • "एरोप्लेन कसे काम करते ते २ परिच्छेदांत समजवा."

3. सर्जनशील (Creative)

   • "१० वर्षांच्या मुलीवर मराठीत पावसावर कविता लिहा."

4. तुलनात्मक (Comparative)

   • "अमेरिका व भारताच्या आर्थिक धोरणांची तुलना तक्त्याच्या स्वरूपात करा."

5. संवादी (Conversational)

   • "तुम्ही प्राचीन रोममधील टूर गाईड आहात असे समजा. शहरातील एक दिवस समजावून सांगा."

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प्रॉम्प्ट इंजिनिअरिंगमधील सामान्य तंत्रे

• Zero-Shot Prompting: उदाहरणांशिवाय काम सोपवणे.

• Few-Shot Prompting: काही उदाहरणे देऊन मार्गदर्शन करणे.

• Chain-of-Thought Prompting: टप्प्याटप्प्याने विचार करण्यास सांगणे.

• Role-based Prompting: विशिष्ट भूमिका घ्यायला लावणे.

• Prompt Templates: पूर्वनिश्चित फॉरमॅट वापरणे.

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उत्तम प्रॉम्प्ट्ससाठी टिप्स

• साधे सुरू करा व हळूहळू सुधारणा करा.

• मर्यादा द्या (उदा. १०० शब्दांत उत्तर द्या).

• अवघड काम छोटे टप्प्यात विभाजित करा.

• आउटपुट तपासा आणि पुन्हा प्रयत्न करा.

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प्रॉम्प्टिंगची उदाहरणे

• मूलभूत: "न्यूटनचे नियम समजवा."

• सुधारलेले: "न्यूटनचे तीन गतीचे नियम १० वर्षांच्या मुलाला समजेल अशा सोप्या भाषेत समजवा."

• फॉरमॅटेड: "सौर उर्जेचे फायदे बुलेट पॉइंट्समध्ये लिहा."

• भूमिकेसह: "तुम्ही शेफ आहात. पालक व चण्यांपासून एक हेल्दी रेसिपी द्या."

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प्रॉम्प्ट इंजिनिअरिंगमधील आव्हाने

• अस्पष्ट प्रश्न = अनिश्चित उत्तरे

• चुकीची माहिती (Hallucinations)

• टोकन मर्यादा

• पक्षपात व नैतिकता

• उत्तरांमध्ये सातत्य नसणे

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प्रॉम्प्ट इंजिनिअरिंगचा वापर

• सॉफ्टवेअर विकास: कोड जनरेशन, डिबगिंग

• मार्केटिंग: जाहिराती, ईमेल, कंटेंट आयडिया

• शिक्षण: ट्यूशन, लेसन प्लॅनिंग

• संशोधन: पेपर सारांश, गृहितके तयार करणे

• कला: कविता, कथा, आयडिया

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भविष्यातील प्रॉम्प्ट इंजिनिअरिंग

• प्रॉम्प्ट प्रोग्रॅमिंग भाषा

• मल्टी-मोडल प्रॉम्प्टिंग (टेक्स्ट + इमेज + ऑडिओ)

• स्वयंचलित प्रॉम्प्ट ऑप्टिमायझेशन

• अॅप्स व वर्कफ्लोमध्ये एम्बेडेड प्रॉम्प्ट्स

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निष्कर्ष

प्रॉम्प्ट इंजिनिअरिंग हे मानवी हेतू व यंत्राचे उत्तर यांच्यातील दुवा आहे.
हे कौशल्य AI ची खरी क्षमता उघडते व वापरकर्त्याला नेमके हवे तसे परिणाम मिळवून देते.
मूलभूत तत्त्वे समजून घेऊन, विविध तंत्रे वापरून व सराव करून कोणताही व्यक्ती या आधुनिक कौशल्यात प्रावीण्य मिळवू शकतो.

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👉 इंग्रजी आवृत्तीसाठी लिंक:  Read Prompt Engineering in English
✍️ लेखक: अजय के. बर्वे

Agentic AI Mastery: From Zero to Pro — The Brain of the Agent (Module- 3)

 

📌 Module 3: The Brain of the Agent — LLM Fundamentals

1. Theory

Large Language Models (LLMs) are at the heart of most modern AI agents.
They process text, reason about it, and generate responses that guide the agent’s actions. 

 

Key Concepts

• Tokenization → Breaking text into smaller units the model can understand.
• Embeddings → Vector representations of text for semantic understanding.
• Context Window → The limit on how much information the LLM can “see” at once.
• Prompt Engineering → Crafting instructions to get desired outputs.

LLM Types

• Local LLMs → Run entirely on your machine (e.g., LLaMA, Mistral)
• Cloud-based LLMs → Accessed via APIs (e.g., OpenAI GPT-4, Anthropic Claude)

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2. Step-by-Step Windows Setup (For This Module)

1. Install Transformers Library

2.  pip install transformers

3.  pip install sentence-transformers

4. Download a Small Local Model (for quick testing)

5.  from transformers import pipeline

6.  gen = pipeline("text-generation", model="distilgpt2")

7.  print(gen("Agentic AI is", max_length=20))

8. Set Up an Embeddings Model

9.  from sentence_transformers import SentenceTransformer

10.model = SentenceTransformer('all-MiniLM-L6-v2')

11.embeddings = model.encode("Agentic AI learns and acts")

12.print(embeddings[:5])

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3. Examples

Example 1 — Few-Shot Prompt for Classification

from transformers import pipeline

classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

print(classifier("Build an agent that schedules meetings", candidate_labels=["Productivity", "Gaming", "Education"]))

Example 2 — Summarizing a News Article

summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

print(summarizer("Artificial Intelligence is transforming industries...", max_length=40, min_length=10))

Example 3 — Semantic Search Using Embeddings

from sklearn.metrics.pairwise import cosine_similarity

docs = ["AI helps businesses", "Cooking pasta", "Agentic AI automates tasks"]

query = "automation in AI"

doc_embeddings = [model.encode(doc) for doc in docs]

query_embedding = model.encode(query)

scores = cosine_similarity([query_embedding], doc_embeddings)

print(scores)

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4. Exercises

1. Create a prompt that classifies user queries into “Tech” or “Non-Tech”.
2. Build a summarizer for PDF documents.
3. Use embeddings to find the most relevant FAQ answer to a user’s question.

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5. Best Practices

• Always test with small models before switching to expensive ones.
• Optimize prompts for clarity and structure.

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6. Common Mistakes

• Sending too much data beyond the context window → truncated outputs.
• Using embeddings from one model with another model for similarity search.

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7. Quiz

1. What is the purpose of embeddings in LLMs?
2. What’s the difference between few-shot and zero-shot classification?
3. Why is the context window important?

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Agentic AI Mastery: From Zero to Pro — A Complete Guide (Module-2)

📌 Module 2: Your AI Workbench — Setting Up on Windows

1. Theory

A well-configured environment is the foundation for building and running Agentic AI applications efficiently.
On Windows, this means:

• Installing the right tools (Python, Git, IDEs)
• Managing virtual environments
• Installing dependencies
• Setting up local or cloud-based LLMs

A proper setup ensures reproducibility — you and others can run the same code with minimal issues.

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Why Windows Setup Matters for AI Development

• Many developers in enterprises use Windows by default.
• With WSL2 or native Python, you can still run modern AI frameworks.
• Windows allows both local LLM execution and cloud API integration.

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2. Step-by-Step Windows Setup (For This Module)

1. Install Python 3.10+
• Download: https://www.python.org/downloads/
• During installation → Check "Add Python to PATH"
2. Install Git
• Download: https://git-scm.com/download/win
3. Install VS Code
• Download: https://code.visualstudio.com/
• Add Python and Git extensions
4. Install Ollama for Local LLMs
• Download: https://ollama.com/download
• Verify with:

o   ollama run llama2

5. Create Virtual Environment

6.  python -m venv agentic_env

7.  .\agentic_env\Scripts\activate

8. Install Libraries

9.  pip install langchain openai requests wikipedia python-dotenv

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3. Examples

Example 1 — Running a Local LLM

• Run:

·       ollama run llama2

• Type:

·       What is Agentic AI?

Example 2 — Testing LangChain Installation

from langchain.prompts import PromptTemplate

template = PromptTemplate(input_variables=["name"], template="Hello {name}, welcome to Agentic AI!")

print(template.format(name="Ajay"))

Example 3 — Using Hugging Face Transformers Locally

from transformers import pipeline

qa = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")

print(qa(question="What is AI?", context="AI stands for Artificial Intelligence."))

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4. Exercises

1. Install a different local model in Ollama.
2. Create a Python script that checks if all dependencies are installed.
3. Set up a VS Code workspace for an Agentic AI project.

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5. Best Practices

• Keep your virtual environment per project.
• Use requirements.txt to track dependencies.

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6. Common Mistakes

• Forgetting to activate the venv before installing packages.
• Using system Python instead of project-specific venv.

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7. Quiz

1. Which command activates a virtual environment in PowerShell?
2. Name two benefits of using Ollama locally.
3. Why should you keep a requirements.txt file?

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