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Rahul Agarwal

Founder | Agentic AI...ย โ€ขย 1m

3 ways how most AI systems are built. Iโ€™ve explained each one step-by-step. 1) ๐—ง๐—ฟ๐—ฎ๐—ฑ๐—ถ๐˜๐—ถ๐—ผ๐—ป๐—ฎ๐—น ๐—”๐—œ (๐˜€๐˜๐—ฒ๐—ฝ-๐—ฏ๐˜†-๐˜€๐˜๐—ฒ๐—ฝ) 1. ๐—ฆ๐—ฒ๐˜ ๐˜๐—ฎ๐˜€๐—ธ โ€“ Decide what problem the model should solve. 2. ๐—–๐—ผ๐—น๐—น๐—ฒ๐—ฐ๐˜ ๐—ฑ๐—ฎ๐˜๐—ฎ โ€“ Gather lots of examples. 3. ๐—ฃ๐—ฟ๐—ฒ๐—ฝ๐—ฎ๐—ฟ๐—ฒ ๐—ฑ๐—ฎ๐˜๐—ฎ โ€“ Clean and label it so the model learns correctly. 4. ๐—•๐˜‚๐—ถ๐—น๐—ฑ ๐—ถ๐—ป๐—ฑ๐—ฒ๐˜… โ€“ Make data searchable with embeddings. 5. ๐—ง๐—ฟ๐—ฎ๐—ถ๐—ป ๐—บ๐—ผ๐—ฑ๐—ฒ๐—น โ€“ Teach the model using the prepared data. 6. ๐——๐—ฒ๐—ฝ๐—น๐—ผ๐˜† โ€“ Put the trained model into use. 7. ๐—š๐—ฒ๐˜ ๐—ฟ๐—ฒ๐˜€๐˜‚๐—น๐˜๐˜€ โ€“ Model answers queries based on training. 8. ๐—˜๐˜ƒ๐—ฎ๐—น๐˜‚๐—ฎ๐˜๐—ฒ โ€“ Measure performance and retrain if needed. _________________________________________________ 2) ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ (๐˜€๐˜๐—ฒ๐—ฝ-๐—ฏ๐˜†-๐˜€๐˜๐—ฒ๐—ฝ) 1. ๐—ฆ๐—ฒ๐˜ ๐—ด๐—ผ๐—ฎ๐—น โ€“ Give the agent a clear objective. 2. ๐—ฃ๐—ถ๐—ฐ๐—ธ ๐—Ÿ๐—Ÿ๐—  โ€“ Use a language model as the agentโ€™s brain. 3. ๐—–๐—ผ๐—ป๐—ป๐—ฒ๐—ฐ๐˜ ๐˜๐—ผ๐—ผ๐—น๐˜€ & ๐—”๐—ฃ๐—œ๐˜€ โ€“ Link it to calendars, browsers, databases, etc. 4. ๐—ฆ๐—ฒ๐—ฎ๐—ฟ๐—ฐ๐—ต & ๐—ณ๐—ฒ๐˜๐—ฐ๐—ต โ€“ Agent can look up info or call external services. 5. ๐—ฃ๐—น๐—ฎ๐—ป ๐˜„๐—ผ๐—ฟ๐—ธ๐—ณ๐—น๐—ผ๐˜„ โ€“ Breaks goal into steps, loops until complete. 6. ๐——๐—ฒ๐—ฐ๐—ถ๐—ฑ๐—ฒ ๐—ฎ๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐˜€ โ€“ Chooses next steps without human input. 7. ๐—˜๐˜…๐—ฒ๐—ฐ๐˜‚๐˜๐—ฒ ๐˜๐—ฎ๐˜€๐—ธ๐˜€ โ€“ Sends emails, runs scripts, calls APIs. 8. ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป & ๐—ถ๐—บ๐—ฝ๐—ฟ๐—ผ๐˜ƒ๐—ฒ โ€“ Adjusts based on outcomes over time. _______________________________________________ 3) ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—ฅ๐—”๐—š (๐˜€๐˜๐—ฒ๐—ฝ-๐—ฏ๐˜†-๐˜€๐˜๐—ฒ๐—ฝ) RAG = ๐—ฅ๐—ฒ๐˜๐—ฟ๐—ถ๐—ฒ๐˜ƒ๐—ฎ๐—น-๐—”๐˜‚๐—ด๐—บ๐—ฒ๐—ป๐˜๐—ฒ๐—ฑ ๐—š๐—ฒ๐—ป๐—ฒ๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป. Agentic RAG combines agentic behavior with fast retrieval of grounded information. 1. ๐—ฆ๐—ฒ๐˜ ๐—ด๐—ผ๐—ฎ๐—น โ€“ Define the task clearly. 2. ๐—ฅ๐—ฒ๐˜๐—ฟ๐—ถ๐—ฒ๐˜ƒ๐—ฒ ๐—ฑ๐—ฎ๐˜๐—ฎ โ€“ Pull relevant docs or knowledge from databases. 3. ๐—ฉ๐—ฒ๐—ฐ๐˜๐—ผ๐—ฟ ๐˜€๐—ฒ๐—ฎ๐—ฟ๐—ฐ๐—ต / ๐—”๐—ฃ๐—œ ๐—ฐ๐—ฎ๐—น๐—น๐˜€ โ€“ Use embeddings and search indexes to find exact, relevant facts. 4. ๐——๐—ฒ๐˜€๐—ถ๐—ด๐—ป ๐—บ๐˜‚๐—น๐˜๐—ถ-๐˜€๐˜๐—ฒ๐—ฝ ๐—ฝ๐—ฟ๐—ผ๐—ฐ๐—ฒ๐˜€๐˜€: Agent plans steps using the retrieved knowledge. 5. ๐—Ÿ๐—ผ๐—ผ๐—ฝ ๐—น๐—ผ๐—ด๐—ถ๐—ฐ โ€“ Retrieve โ†’ reason โ†’ act โ†’ verify. 6. ๐—œ๐—บ๐—ฝ๐—น๐—ฒ๐—บ๐—ฒ๐—ป๐˜ ๐—ฎ๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐˜€ & ๐—พ๐˜‚๐—ฒ๐—ฟ๐˜† ๐—”๐—ฃ๐—œ๐˜€: Carry out actions (call services, update DB). 7. ๐—–๐—ต๐—ฒ๐—ฐ๐—ธ ๐—ฟ๐—ฒ๐˜€๐˜‚๐—น๐˜๐˜€ โ€“ Verify answers against facts for accuracy. 8. ๐—ฅ๐—ฒ๐—ณ๐—ฟ๐—ฒ๐˜€๐—ต ๐—บ๐—ฒ๐—บ๐—ผ๐—ฟ๐˜† โ€“ Save outcomes into memory or vector DB so future tasks use updated info. 9. ๐—”๐—ฑ๐—ฎ๐—ฝ๐˜ โ€“ System improves over time with stored results. _____________________________________________ โœ… Which to pick? โ€ข Use ๐—ง๐—ฟ๐—ฎ๐—ฑ๐—ถ๐˜๐—ถ๐—ผ๐—ป๐—ฎ๐—น ๐—”๐—œ when task is narrow, stable, and must be highly reliable (e.g., image recognition in a controlled domain). โ€ข Use ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—”๐—œ when automation of multi-step work is needed (scheduling, orchestration, admin tasks). โ€ข Use ๐—”๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐—ฅ๐—”๐—š when you need both action and factually grounded responses drawn from up-to-date data (customer support, codebase assistant, enterprise knowledge work). โœ… Repost this for others in your network.

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