Referred Link - https://www.linkedin.com/posts/the-gen-academy_genacademy-genai-rag-activity-7374314296928899072-m3Z5

𝗧𝗵𝗶𝗻𝗸 𝗼𝗳 𝗥𝗔𝗚 𝗮𝘀 𝗴𝗶𝘃𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗔𝗜 𝗽𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝘁𝗼 “𝗼𝗽𝗲𝗻 𝗮 𝗯𝗼𝗼𝗸” 𝗯𝗲𝗳𝗼𝗿𝗲 𝗶𝘁 𝗮𝗻𝘀𝘄𝗲𝗿𝘀.

If you’ve bumped into Retrieval-Augmented Generation (RAG) and wondered what it really is (and when you actually need it), this mini-primer is for you.

𝗪𝗵𝗮𝘁 𝗥𝗔𝗚 𝗶𝘀 — 𝗶𝗻 𝗼𝗻𝗲 𝗯𝗿𝗲𝗮𝘁𝗵

RAG pairs a language model with an external knowledge source so answers are grounded in real, up-to-date information instead of just whatever the model remembers from training. That means fewer made-up facts and more verifiable responses.

𝗪𝗵𝗲𝗻 𝘆𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱 𝗿𝗲𝗮𝗰𝗵 𝗳𝗼𝗿 𝗥𝗔𝗚
✅You want a domain-specific assistant (HR policy bot, clinical FAQ, internal IT helper).
✅You need current info beyond a model’s training cutoff.
✅You care about citations and traceability.

𝗧𝗵𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 (𝘀𝗶𝗺𝗽𝗹𝗲 𝘃𝗲𝗿𝘀𝗶𝗼𝗻)
✅𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 – Gather your sources (PDFs, sites, databases). Split long docs into smaller, meaningful “chunks,” turn each chunk into an embedding (a numeric vector), and store them in a vector database for fast similarity search.

✅𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 – Convert the user’s question into an embedding and fetch the closest chunks from the vector store.

✅𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 – Feed the question + retrieved chunks to the LLM to produce a grounded answer (and optionally add citations).

Why chunk? Models don’t magically use long context well; narrowing to the most relevant bits improves precision and keeps prompts lean.

𝗛𝗲𝗹𝗽𝗳𝘂𝗹 𝗮𝗱𝗱-𝗼𝗻𝘀 (𝘂𝘀𝗲 𝗮𝘀 𝗻𝗲𝗲𝗱𝗲𝗱)

✅𝗤𝘂𝗲𝗿𝘆 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻 (𝗛𝘆𝗗𝗘, 𝗺𝘂𝗹𝘁𝗶-𝗾𝘂𝗲𝗿𝘆): Rewrite or expand the question so retrieval finds better matches. HyDE, for instance, has the model draft a hypothetical answer, embed it, and search with that to boost recall.

✅𝗥𝗼𝘂𝘁𝗶𝗻𝗴 & 𝗰𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻: If you have multiple stores (policies, product docs, web search), route the query to the best source and add filters (e.g., “last 90 days”).

𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗹𝗼𝗰𝗸𝘀 (𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘁𝗵𝗲 𝗵𝗲𝗮𝗱𝗮𝗰𝗵𝗲)
✅ 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 (𝗰𝗵𝗮𝗶𝗻𝘀): Wire steps like “translate → retrieve → generate → parse” into a clear sequence you can swap and test.

✅𝗟𝗮𝗻𝗴𝗦𝗺𝗶𝘁𝗵 (𝗼𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆): Trace every run, see timings and inputs/outputs, and debug failures—super handy once you go beyond demos.

𝗦𝘂𝗺𝗺𝗮𝗿𝘆 𝘆𝗼𝘂 𝗰𝗮𝗻 𝘁𝗮𝗸𝗲 𝘁𝗼 𝘄𝗼𝗿𝗸
✅Start simple: good chunking + a solid vector DB + a clear prompt template.
✅Measure what matters (accuracy on real tasks, not vibes).
✅Iterate: logs and traces will tell you where the bottleneck is.

Tags: 

#genai #rag 

 Referred Link - https://www.linkedin.com/posts/prem-natarajan-ai_%F0%9D%90%8F%F0%9D%90%AB%F0%9D%90%A8%F0%9D%90%A6%F0%9D%90%A9%F0%9D%90%AD%F0%9D%90%A2%F0%9D%90%A7%F0%9D%90%A0-%F0%9D%90%9A%F0%9D%90%AD%F0%9D%90%AD%F0%9D%90%9A%F0%9D%90%9C%F0%9D%90%A4%F0%9D%90%AC-%F0%9D%90%9A%F0%9D%90%AB%F0%9D%90%9E-activity-7377318581971165184-hL-R/

𝐏𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠 𝐚𝐭𝐭𝐚𝐜𝐤𝐬 𝐚𝐫𝐞 𝐭𝐡𝐞 𝐧𝐞𝐰 𝐭𝐲𝐩𝐞 𝐨𝐟 𝐀𝐈 𝐫𝐢𝐬𝐤𝐬.
While LLMs are powerful, they are also vulnerable to clever manipulations that bypass safeguards, expose sensitive data, or distort outputs.

Understanding these attacks is critical - not just for researchers, but also for businesses deploying AI. Here’s a breakdown of the major types of prompting attacks and how they operate:

🔑 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐏𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠 𝐀𝐭𝐭𝐚𝐜𝐤𝐬

1. Jailbreaks (Safety Bypass)
Trick the model into ignoring built-in rules or safety policies to return disallowed content.

2. Prompt Injection
Hide malicious commands inside external content so the model unknowingly executes them.

3. Instruction Overriding / Role Abuse
Convince the model to adopt roles or personas that override its safety checks.

4. Chained / Recursive Prompting
Break big restrictions into small prompts executed step-by-step until rules are bypassed.

5. Resource / Command Injection
Exploit API/tool access by forcing repeated costly, unauthorized, or harmful operations.

6. Prompt Leakage / Chain-of-Trust Attacks
Trick models into exposing hidden system prompts, policies, or internal instructions.

7. Adversarial Examples (Input Perturbation)
Slight text tweaks (like punctuation changes) that confuse parsing and produce wrong outputs.

8. Trojan / Backdoor Triggers
Hidden phrases or patterns that trigger abnormal model behavior when encountered.

9. Social-Engineering Prompts
Persuasive prompts that manipulate models into generating deceptive or fraudulent outputs.

10. Data Exfiltration via LLMs
Coax models into leaking private or sensitive data from prior context or training.

11. Model Inversion / Membership Inference
Probe models to infer whether specific records were part of their training set.

12. Covert Channels / Steganographic Prompts
Hide malicious instructions in harmless-looking text or encoded patterns.

Prompting attacks show that AI security is not just about model training - it is about prompt design, monitoring, and safeguards.
The more structured and layered your defences, the harder it becomes for attackers to exploit these vulnerabilities.

Tags:  

#ArtificialIntelligence, #AI, #ChatGPT, #LLM 

 Referred Link - https://www.linkedin.com/posts/goyalshalini_ai-agents-vs-agentic-ai-whats-the-real-activity-7340626798772166656-Ceeg

Tags:

 #ArtificialIntelligence, #AgenticAI, #NewTechnology,

Referred Link - https://www.linkedin.com/posts/vsadhwani_if-youre-looking-to-get-hands-on-with-cloud-activity-7333878302664712193-cbOm

 Referred Link - https://www.linkedin.com/feed/update/urn:li:activity:7330961599794425856/

These 8 channels will teach you more than any university degree 👇

Don’t waste time scrolling through endless ML/AI tutorials – these are the ones worth your time:

1.) Andrej Karpathy: https://lnkd.in/gcbV65Qd
Former Tesla AI Director and OpenAI cofounder who builds neural networks from scratch with clear code anyone can follow. His "Zero to Hero" series is legendary for making deep learning accessible.

2.) sentdex: https://lnkd.in/gt42jCgd
Harrison Kinsley creates real AI applications showing every step from blank file to working solution. Covers everything from stock prediction to computer vision with practical Python code.

3.) 3Blue1Brown: https://lnkd.in/gzjCSA-E
Grant Sanderson delivers stunning visual explanations of math concepts underlying ML. His neural network series makes complex math easy to understand.

4.) Krish Naik: https://lnkd.in/gDc4gbfm
Industry practitioner providing end-to-end ML project tutorials with coding exercises. Great for data scientists looking for implementation-focused content.

5.) Yannic Kilcher: https://lnkd.in/gdnNE32V
Explains the latest AI research papers in the simple terms the same day they're released. Breaks down transformers, diffusion models, and reinforcement learning without the academic jargon.

6.) Serrano Academy: https://lnkd.in/gTGaUSmY
Luis Serrano explains complex ML concepts using simple analogies and visualizations. Perfect for beginners wanting to grasp fundamental concepts.

7.) DeepLearningAI: https://lnkd.in/guPvZuCt
Andrew Ng's channel offering structured courses on ML/DL fundamentals. Features interviews with AI leaders and practical tutorials from industry experts.

8.) MIT OpenCourseWare: https://lnkd.in/gQpAgCEb
Top-tier university-level courses on math foundations powering modern ML. Connects theory to practice, explaining why algorithms work, not just how to use them.

Tags:

#ArtificialIntelligence, #MachineLearning, #E-Learning, #FreshersLearning,