Build a RAG system in 5 steps: (1) Prepare your documents, (2) Create embeddings using OpenAI, (3) Store in Pinecone vector database, (4) Build retrieval chain with LangChain, (5) Connect to an LLM for generation. This lets your AI chat with your own documents accurately.
Read MoreTop GenAI skills employers want: prompt engineering, RAG architecture, LangChain development, API integration, AI agent building, fine-tuning, vector databases, Python, and cloud deployment. Our analysis of 200+ job postings shows these skills appear most frequently in GenAI job listings.
Read MoreAI certifications worth considering: Google Cloud AI, AWS Machine Learning, DeepLearning.AI, and specialised bootcamp certificates. However, employers increasingly value portfolios over certificates. The best approach: a course that gives you both a certificate AND a GitHub portfolio of real projects.
Read MoreTop GenAI portfolio projects: RAG-based Document Q&A chatbot, AI customer support agent, autonomous research agent, content generation pipeline, smart email assistant, and AI lead scoring system. Aim for 5-8 projects showing progression from basic to advanced skills.
Read MoreGenerative AI is moving from chatbots to autonomous agents, multimodal systems, and industry-specific solutions. By 2030, AI agents will handle complex business workflows. McKinsey estimates $4.4 trillion in annual value. The key trend: AI is becoming a skill requirement, not a specialty.
Read MoreAI will not replace you, but someone who knows AI might. The World Economic Forum projects 97 million new AI-related jobs. The shift is not job elimination but job transformation. Professionals who learn to work with AI tools will have a decisive advantage across every industry in 2026.
Read MoreFine-tuning is customising a pre-trained large language model for a specific domain or task using your own data. Techniques like LoRA and QLoRA allow fine-tuning with minimal compute. Use fine-tuning when prompt engineering alone cannot achieve the required accuracy for specialised domains.
Read MoreKey GenAI terms: LLM (Large Language Model) is AI that processes and generates text. RAG connects AI to your data. Fine-tuning customises a model for your domain. Tokens are the units AI uses to process text. Vector database stores data for AI retrieval. This glossary explains 50+ AI terms in plain language.
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