@Ai_Vaidehi: Steps to building AI systems with LLM's. I've given a simple detailed explanation below. ๐ฆ๐๐ฒ๐ฝ 1 โ ๐๐๐ ๐ (๐๐ฎ๐ฟ๐ด๐ฒ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐)
Summary
A step-by-step guide to building AI systems using LLMs, covering steps from choosing models to evaluation using frameworks, vector databases, and data extraction tools.
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Steps to building AI systems with LLMโs.
Iโve given a simple detailed explanation below.
Step 1 โ LLMs (Large Language Models) โข These are the brains of the system. โข Examples: GPT (OpenAI), Gemini, Claude etc. โข They generate answers, understand queries, and perform reasoning.
Step 2 โ Frameworks โข Frameworks help you connect the LLM with data, tools, and apps. โข Examples: LangChain, Llama Index, Haystack, Txtai. โข They act like a toolkit so you donโt have to build everything from scratch.
Step 3 โ Vector Databases โข LLMs canโt remember everything. They need a memory system. โข Vector databases store โembeddingsโ (numerical representations of text). โข Examples: Pinecone, Weaviate, Chroma, Milvus, Qdrant. โข They make searching fast and relevant (like Google search but for your private data).
Step 4 โ Data Extraction โข Your AI needs real-world data inputs. โข Tools like Crawl4AI, FireCrawl, ScrapeGraphAI, Docling, LlamaParse help:
- Scrape websites
- Extract PDFs, docs, or tables
- Clean and structure messy data
Step 5 โ Open LLMs Access โข Instead of calling proprietary APIs, you can run LLMs locally or via open-source providers. โข Examples: Hugging Face, Ollama etc.
Step 6 โ Text Embeddings โข To store text in databases, you must first convert it into numbers (vectors). โข Tools like OpenAI Embeddings, SBERT, Voyage AI etc handle this. โข Embeddings allow semantic search (finding meaning, not just keywords).
Step 7 โ Evaluation โข Once built, you must test and improve your system. โข Tools: Giskard, Ragas, Trulens. โข They measure:
- Accuracy
- Hallucinations (wrong answers)
- Relevance of results
Final Flow in Simple Words:
- Choose a model (LLM).
- Connect it with a framework.
- Collect data and extract it properly.
- Turn data into embeddings and store them in a vector DB.
- Give the LLM access to search that DB.
- Use open access tools if you want local/cheap models.
- Continuously evaluate and refine.
You can apply this framework in your company to design and deploy powerful AI solutions for your business.
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