Steps to building AI systems with LLM's.
I've given a simple detailed explanation below.
𝗦𝘁𝗲𝗽 1 – 𝗟𝗟𝗠𝘀 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀)
• These are the 𝗯𝗿𝗮𝗶𝗻𝘀 of the system.
• Examples: GPT (OpenAI), Gemini, Claude etc.
• They generate answers, understand queries, and perform reasoning.
𝗦𝘁𝗲𝗽 2 – 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀
• Frameworks help you 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘁𝗵𝗲 𝗟𝗟𝗠 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮, 𝘁𝗼𝗼𝗹𝘀, 𝗮𝗻𝗱 𝗮𝗽𝗽𝘀.
• Examples: LangChain, Llama Index, Haystack, Txtai.
• They act like a 𝘁𝗼𝗼𝗹𝗸𝗶𝘁 so you don’t have to build everything from scratch.
𝗦𝘁𝗲𝗽 3 – 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀
• LLMs can’t remember everything. They need a 𝗺𝗲𝗺𝗼𝗿𝘆 𝘀𝘆𝘀𝘁𝗲𝗺.
• Vector databases store “embeddings” (numerical representations of text).
• Examples: Pinecone, Weaviate, Chroma, Milvus, Qdrant.
• They make searching fast and relevant (like Google search but for your private data).
𝗦𝘁𝗲𝗽 4 – 𝗗𝗮𝘁𝗮 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻
• Your AI needs real-world 𝗱𝗮𝘁𝗮 𝗶𝗻𝗽𝘂𝘁𝘀.
• Tools like Crawl4AI, FireCrawl, ScrapeGraphAI, Docling, LlamaParse help:
- Scrape websites
- Extract PDFs, docs, or tables
- Clean and structure messy data
𝗦𝘁𝗲𝗽 5 – 𝗢𝗽𝗲𝗻 𝗟𝗟𝗠𝘀 𝗔𝗰𝗰𝗲𝘀𝘀
• Instead of calling proprietary APIs, you can 𝗿𝘂𝗻 𝗟𝗟𝗠𝘀 𝗹𝗼𝗰𝗮𝗹𝗹𝘆 or via open-source providers.
• Examples: Hugging Face, Ollama etc.
𝗦𝘁𝗲𝗽 6 – 𝗧𝗲𝘅𝘁 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀
• To store text in databases, you must first 𝗰𝗼𝗻𝘃𝗲𝗿𝘁 𝗶𝘁 𝗶𝗻𝘁𝗼 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 (𝘃𝗲𝗰𝘁𝗼𝗿𝘀).
• Tools like OpenAI Embeddings, SBERT, Voyage AI etc handle this.
• Embeddings allow semantic search (finding meaning, not just keywords).
𝗦𝘁𝗲𝗽 7 – 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻
• Once built, you must 𝘁𝗲𝘀𝘁 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 your system.
• Tools: Giskard, Ragas, Trulens.
• They measure:
- Accuracy
- Hallucinations (wrong answers)
- Relevance of results
✅ 𝗙𝗶𝗻𝗮𝗹 𝗙𝗹𝗼𝘄 𝗶𝗻 𝗦𝗶𝗺𝗽𝗹𝗲 𝗪𝗼𝗿𝗱𝘀:
1. Choose a model (LLM).
2. Connect it with a framework.
3. Collect data and extract it properly.
4. Turn data into embeddings and store them in a vector DB.
5. Give the LLM access to search that DB.
6. Use open access tools if you want local/cheap models.
7. Continuously evaluate and refine.
You can apply this framework in your company to design and deploy powerful AI solutions for your business.
🔖 Save for later.
♻️ Repost to help other engineers learn and grow.
The AI infrastructure boom is just getting started
Top AI-related stocks gaining attention:
🔹 $NVDA – AI chips
🔹 $MSFT – Cloud & Copilot
🔹 $PLTR – AI-powered analytics
🔹 $CRWD – AI cybersecurity
🔹 $SNOW – Enterprise AI data platform
🔹 $TSM – Advanced chip manufacturing
🔹 $ARM – AI architecture backbone
🔹 $HPE – AI infrastructure
🔹 $DELL – Enterprise AI deployment
🔹 $CRWV – AI cloud infrastructure
🔹 $IREN – AI-focused energy & compute
The AI race isn't about one winner—it's about the entire ecosystem
On SensaMarket, build and analyze 100+ options strategies with real-time Greeks analytics (GEX, DEX, VEX) and live institutional flow data—helping traders spot opportunities before the crowd - https://t.co/sdgZEW4q2I
미국, 일본 환율이 너무 오르네요. 그만큼 원화가치는 떨어지는데... 예전에 "정부가 이런 식으로 관리하지말라고 했던 분이"... 지금은 조용하네요. 부동산 세금도 어느정부에서 선거 전에 올리겠다고 선전포고룰 하는지요?? 선거뿐 아미라, 부동산, 환율정책 등 손놓고 있네요.
जानवर सिर्फ बोल नहीं सकते…
लेकिन दर्द, प्यार और अपनापन वो भी महसूस करते हैं। 🐾❤️
मुसीबत में उनके लिए जो इंसान रक्षक बनकर खड़ा होता है,
वही सच्ची इंसानियत दिखाता है। 🙏
#AnimalLovers#Humanity