Founder | Agentic AI...ย โขย 1m
3 ways how most AI systems are built. Iโve explained each one step-by-step. 1) ๐ง๐ฟ๐ฎ๐ฑ๐ถ๐๐ถ๐ผ๐ป๐ฎ๐น ๐๐ (๐๐๐ฒ๐ฝ-๐ฏ๐-๐๐๐ฒ๐ฝ) 1. ๐ฆ๐ฒ๐ ๐๐ฎ๐๐ธ โ Decide what problem the model should solve. 2. ๐๐ผ๐น๐น๐ฒ๐ฐ๐ ๐ฑ๐ฎ๐๐ฎ โ Gather lots of examples. 3. ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฒ ๐ฑ๐ฎ๐๐ฎ โ Clean and label it so the model learns correctly. 4. ๐๐๐ถ๐น๐ฑ ๐ถ๐ป๐ฑ๐ฒ๐ โ Make data searchable with embeddings. 5. ๐ง๐ฟ๐ฎ๐ถ๐ป ๐บ๐ผ๐ฑ๐ฒ๐น โ Teach the model using the prepared data. 6. ๐๐ฒ๐ฝ๐น๐ผ๐ โ Put the trained model into use. 7. ๐๐ฒ๐ ๐ฟ๐ฒ๐๐๐น๐๐ โ Model answers queries based on training. 8. ๐๐๐ฎ๐น๐๐ฎ๐๐ฒ โ Measure performance and retrain if needed. _________________________________________________ 2) ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ (๐๐๐ฒ๐ฝ-๐ฏ๐-๐๐๐ฒ๐ฝ) 1. ๐ฆ๐ฒ๐ ๐ด๐ผ๐ฎ๐น โ Give the agent a clear objective. 2. ๐ฃ๐ถ๐ฐ๐ธ ๐๐๐ โ Use a language model as the agentโs brain. 3. ๐๐ผ๐ป๐ป๐ฒ๐ฐ๐ ๐๐ผ๐ผ๐น๐ & ๐๐ฃ๐๐ โ Link it to calendars, browsers, databases, etc. 4. ๐ฆ๐ฒ๐ฎ๐ฟ๐ฐ๐ต & ๐ณ๐ฒ๐๐ฐ๐ต โ Agent can look up info or call external services. 5. ๐ฃ๐น๐ฎ๐ป ๐๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐ โ Breaks goal into steps, loops until complete. 6. ๐๐ฒ๐ฐ๐ถ๐ฑ๐ฒ ๐ฎ๐ฐ๐๐ถ๐ผ๐ป๐ โ Chooses next steps without human input. 7. ๐๐ ๐ฒ๐ฐ๐๐๐ฒ ๐๐ฎ๐๐ธ๐ โ Sends emails, runs scripts, calls APIs. 8. ๐๐ฒ๐ฎ๐ฟ๐ป & ๐ถ๐บ๐ฝ๐ฟ๐ผ๐๐ฒ โ Adjusts based on outcomes over time. _______________________________________________ 3) ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐ฅ๐๐ (๐๐๐ฒ๐ฝ-๐ฏ๐-๐๐๐ฒ๐ฝ) RAG = ๐ฅ๐ฒ๐๐ฟ๐ถ๐ฒ๐๐ฎ๐น-๐๐๐ด๐บ๐ฒ๐ป๐๐ฒ๐ฑ ๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป. Agentic RAG combines agentic behavior with fast retrieval of grounded information. 1. ๐ฆ๐ฒ๐ ๐ด๐ผ๐ฎ๐น โ Define the task clearly. 2. ๐ฅ๐ฒ๐๐ฟ๐ถ๐ฒ๐๐ฒ ๐ฑ๐ฎ๐๐ฎ โ Pull relevant docs or knowledge from databases. 3. ๐ฉ๐ฒ๐ฐ๐๐ผ๐ฟ ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต / ๐๐ฃ๐ ๐ฐ๐ฎ๐น๐น๐ โ Use embeddings and search indexes to find exact, relevant facts. 4. ๐๐ฒ๐๐ถ๐ด๐ป ๐บ๐๐น๐๐ถ-๐๐๐ฒ๐ฝ ๐ฝ๐ฟ๐ผ๐ฐ๐ฒ๐๐: Agent plans steps using the retrieved knowledge. 5. ๐๐ผ๐ผ๐ฝ ๐น๐ผ๐ด๐ถ๐ฐ โ Retrieve โ reason โ act โ verify. 6. ๐๐บ๐ฝ๐น๐ฒ๐บ๐ฒ๐ป๐ ๐ฎ๐ฐ๐๐ถ๐ผ๐ป๐ & ๐พ๐๐ฒ๐ฟ๐ ๐๐ฃ๐๐: Carry out actions (call services, update DB). 7. ๐๐ต๐ฒ๐ฐ๐ธ ๐ฟ๐ฒ๐๐๐น๐๐ โ Verify answers against facts for accuracy. 8. ๐ฅ๐ฒ๐ณ๐ฟ๐ฒ๐๐ต ๐บ๐ฒ๐บ๐ผ๐ฟ๐ โ Save outcomes into memory or vector DB so future tasks use updated info. 9. ๐๐ฑ๐ฎ๐ฝ๐ โ System improves over time with stored results. _____________________________________________ โ Which to pick? โข Use ๐ง๐ฟ๐ฎ๐ฑ๐ถ๐๐ถ๐ผ๐ป๐ฎ๐น ๐๐ when task is narrow, stable, and must be highly reliable (e.g., image recognition in a controlled domain). โข Use ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐๐ when automation of multi-step work is needed (scheduling, orchestration, admin tasks). โข Use ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐ฅ๐๐ when you need both action and factually grounded responses drawn from up-to-date data (customer support, codebase assistant, enterprise knowledge work). โ Repost this for others in your network.

ย โขย
Medialย โขย 4m
๐๐ ๐๐๐ฎ๐ฟ๐๐๐ฝ๐ ๐ฐ๐ฎ๐ปโ๐ ๐๐๐ฟ๐๐ถ๐๐ฒ ๐ผ๐ป ๐๐ต๐ฒ ๐ผ๐น๐ฑ ๐ฆ๐ฎ๐ฎ๐ฆ ๐ฝ๐ฟ๐ถ๐ฐ๐ถ๐ป๐ด ๐บ๐ผ๐ฑ๐ฒ๐น. Why? Because AI costs arenโt fixed. Things like: GPU time API calls Token processing Video rendering ...all get more expensive as users do
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ย โขย
Set2Scoreย โขย 5m
๐ง๐ต๐ถ๐ ๐๐ ๐๐๐ฎ๐ฟ๐๐๐ฝ ๐ถ๐ ๐ฐ๐ต๐ผ๐ผ๐๐ฒ๐ป ๐ฏ๐ ๐๐ข๐ ๐๐ผ ๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐๐ป๐ฑ๐ถ๐ฎ' ๐ณ๐ถ๐ฟ๐๐ ๐ป๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฒ๐ฟ๐ฒ๐ถ๐ด๐ป ๐๐๐ !! Sarvam AI โ Bulbul Initiative : Bulbul V2 is a text-to-speech (TTS) model developed specifically for the
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Founder | Agentic AI...ย โขย 28d
Fine-tune vs Prompt vs Context Engineering. Simple step-by-step breakdown for each approach. ๐๐ถ๐ป๐ฒ-๐ง๐๐ป๐ถ๐ป๐ด (๐ ๐ผ๐ฑ๐ฒ๐น-๐๐ฒ๐๐ฒ๐น ๐๐๐๐๐ผ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป) ๐๐น๐ผ๐: 1. Collect Data โ Gather domain-specific info (e.g., legal docs). 2. Sta
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ย โขย
Medialย โขย 6m
๐ ๐ผ๐๐ ๐ฝ๐ฒ๐ผ๐ฝ๐น๐ฒ ๐๐ต๐ถ๐ป๐ธ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐ฏ๐๐๐ ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐. Wrong. Microsoft buys ๐ฆ๐จ๐ฆ๐๐ง๐ญ๐ฎ๐ฆ. ๐๐ผ๐ผ๐ธ ๐ฎ๐ ๐๐ต๐ฒ๐ถ๐ฟ ๐ฏ๐ถ๐ด๐ด๐ฒ๐๐ ๐บ๐ผ๐๐ฒ๐: ๐. ๐๐จ๐ญ๐ฆ๐๐ข๐ฅ (๐๐๐๐) โ $๐๐๐๐ โ 10M users in 18 months โ
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ย โขย
Set2Scoreย โขย 9m
๐๐ฟ๐ฒ ๐๐ผ๐ ๐๐๐ถ๐ป๐ด ๐๐ต๐ฒ๐๐ฒ ๐๐ผ๐ฝ ๐๐ ๐ฎ๐ฝ๐ฝ๐ ? If not then it's your turn now.... โณ ๐ฆ๐ฒ๐ฎ๐ฟ๐ฐ๐ต & ๐ฅ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต: ChatGPT : Conversational Al chat bot for generating text Claude : Best for code & detailed problem-solving DeepSeek:
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Founder | Agentic AI...ย โขย 2m
Simple explanation of Traditional RAG vs Agentic RAG vs MCP. 1. ๐ง๐ฟ๐ฎ๐ฑ๐ถ๐๐ถ๐ผ๐ป๐ฎ๐น ๐ฅ๐๐ (๐ฅ๐ฒ๐๐ฟ๐ถ๐ฒ๐๐ฎ๐น-๐๐๐ด๐บ๐ฒ๐ป๐๐ฒ๐ฑ ๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป) โข ๐ฆ๐๐ฒ๐ฝ 1: ๐จ๐๐ฒ๐ฟ ๐ฎ๐๐ธ๐ ๐ฎ ๐พ๐๐ฒ๐๐๐ถ๐ผ๐ป. Example: โ๐๐ฉ๐ข๐ต ๐ช๐ด ๐ต๐ฉ๐ฆ ๐ค๐ข๐ฑ๐ช๏ฟฝ
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ย โขย
Set2Scoreย โขย 6m
๐๐ผ๐ ๐ธ๐ต๐ฎ๐๐ฎ๐ฏ๐ผ๐ผ๐ธ ๐๐๐ฎ๐ฟ๐๐๐ฝ ๐ฏ๐ฒ๐ฐ๐ฎ๐บ๐ฒ ๐ป๐ผ ๐ญ ๐ฐ๐ต๐ผ๐ถ๐ฐ๐ฒ ๐ณ๐ผ๐ฟ ๐ฏ๐๐๐ถ๐ป๐ฒ๐๐๐บ๐ฎ๐ป ๐๐ผ ๐ธ๐ฒ๐ฒ๐ฝ ๐๐ต๐ฒ๐ฟ๐ฒ ๐ฑ๐ฎ๐๐ฎ ? Founded by Ravish Naresh, who previously co-founded Housing.com Vision to digitize bookkeeping for small an
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ย โขย
Medialย โขย 6m
๐ช๐ต๐ ๐ ๐ฒ๐๐ฎ ๐ข๐ฝ๐ฒ๐ป-๐ฆ๐ผ๐๐ฟ๐ฐ๐ฒ๐ ๐๐๐ ๐๐ ๐ ๐ผ๐ฑ๐ฒ๐น๐ โ ๐๐ป๐ฑ ๐ช๐ต๐ฎ๐ ๐๐ ๐ง๐ฒ๐ฎ๐ฐ๐ต๐ฒ๐ ๐จ๐ ๐๐ฏ๐ผ๐๐ ๐ฃ๐ผ๐๐ฒ๐ฟ ๐ถ๐ป ๐๐๐๐ถ๐ป๐ฒ๐๐ Meta (Facebookโs parent company) does something most AI companies donโt: ๐๐ญ ๐ ๐ข๐ฏ๐๐ฌ ๐๐ฐ๐๏ฟฝ
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