nikosv

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.