What will you in Open-source LLMs: Uncensored & secure AI locally with RAG Course
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Explore the advantages and limitations of open-source vs closed-source LLMs (e.g., Llama, Mistral, Phi‑3, Qwen)
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Install and run LLMs locally using tools like LM Studio, Ollama, and Anything LLM
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Build custom RAG pipelines with vector databases, embedding models, and function calling
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Employ prompt-engineering strategies, system prompts, and agents (e.g., Flowise)
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Fine‑tune models (Alpaca, Llama‑3) via Google Colab and manage hardware and GPU usage
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Understand AI security: jailbreaks, prompt injections, data poisoning, and privacy risks
Program Overview
Module 1: Why Open-Source LLMs
⏳ 30 minutes
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Compare open- and closed-source model pros/cons (ownership, censorship, cost)
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Survey popular open LLMs: Llama3, Mistral, Grok, Phi‑3, Gemma, Qwen
Module 2: Local Deployment & Tools
⏳ 60 minutes
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Set up LM Studio, Anything LLM, Ollama locally using CPU/GPU; hardware requirements explained
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Distinguish between censored vs uncensored models
Module 3: Prompt Engineering & Function Calling
⏳ 60 minutes
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Learn system prompts, structured prompts, few-shot, chain-of-thought techniques
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Use function-calling in Llama3 and Anything LLM for chatbots and data pipelines
Module 4: RAG & Vector Databases
⏳ 75 minutes
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Build local RAG chatbot using LM Studio and embedding store
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Integrate Firecrawl (web scraping), LlamaIndex/LlamaParse for PDF/CSV ingestion
Module 5: AI Agents & Flowise
⏳ 60 minutes
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Define AI agents and set up multi-agent workflows with Flowise locally
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Create intelligent agents that generate Python code, documentation, and interface with APIs
Module 6: Fine‑Tuning & GPU Rental
⏳ 60 minutes
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Fine-tune on Alpaca and Llama‑3 via Google Colab; information on using Runpod or Massed Compute
Module 7: TTS, Hosting & Extras
⏳ 45 minutes
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Implement text-to-speech (TTS) solutions using Colab; self-hosting options and agent selection advice
Module 8: Security, Privacy & Scaling
⏳ 45 minutes
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Learn about jailbreaks, prompt injections, data poisoning, and content leakage risks
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Explore commercial policies, data privacy, and secure deployment best practices
Get certificate
Job Outlook
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High demand for engineers skilled in self-hosted, privacy-focused AI, particularly for RAG and LLM agents
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Fostered careers in AI infrastructure, data engineering, and developer tooling
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Salary potential: $110K–$180K+ for LLM engineering roles with RAG and security focus
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Freelance paths include custom RAG solutions, privacy-first chatbot deployment, and AI-agent consulting
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Related Courses
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Generative AI Engineering with LLMs Specialization – Learn how to design, build, and deploy generative AI applications using large language models for practical, real-world solutions.
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Introduction to Large Language Models – Gain foundational knowledge of LLMs, their architecture, capabilities, and how they can be applied securely in various applications.
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Intro to Large Language Models (LLMs) – Explore the basics of open-source LLMs and learn how to implement them locally for secure and customizable AI workflows.
Related Reading
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What Is Product Management? – Discover how product management principles guide the successful design, deployment, and scaling of AI and LLM-based applications.
