LLM - Generative AI key concepts for beginner

After a year as a GenAI PM, here are few concepts aka "buzz words," that help me in conversations with our AI team while building a Gen AI-powered mental health product. 

Worth mentioning that this is not the exhaustive list of Gen AI terms & definitions, this is the 'MVP' version of it, highly recommend newbie to use it as a pointer to start your Gen AI journey.

1. Concept & Cost

• LLM (Large Language Model): AI models trained on vast text datasets for generating human-like language, often the backbone of conversational systems. Latest models are multimodal, combining text, image, code, and audio capabilities.

• Generative AI: Systems that create new content (text, images, audio, code). Generative AI now enables scientific discovery, generates synthetic data for privacy, and is used for real-time applications (e.g., live translation).

• Hallucination: Factually incorrect or nonsensical outputs. Newer models use retrieval and real-time fact-checking to mitigate this.

• Vector Database: Stores high-dimensional embeddings for efficient similarity search and retrieval—crucial for grounding AI responses and reducing hallucinations. Now foundational for RAG pipelines and multimodal data.

• Zero-Shot, One-Shot, Few-Shot Learning: Prompting techniques:

  • Zero-Shot: Model answers without examples.

  • One-Shot: Learns from one example.

  • Few-Shot: Learns from a small set of examples.

• Token & Cost Calculation: A token is the smallest text unit processed by LLMs; model usage costs are calculated per token in input and output, driving architecture choices towards model compression, distillation, and quantization for cost reduction.

• Model Compression & Efficiency: TinyLLM/TinyGPT and sparse expert models make high performance accessible even on edge devices, with energy-efficient deployment prioritized for sustainability.

  
  

2. Techniques for Implementation

• Retrieval-Augmented Generation (RAG): Enhances LLMs with external data (vector DBs), grounding outputs by combining generative power with accurate retrieval.

• Multi-Agents / Agentic AI: Multiple autonomous agents collaborate or compete—2025 brings “agentic AI,” with LLM-powered agents performing end-to-end tasks and workflow management.

• Turns & Context-Aware AI: "Turn" refers to each conversational exchange. Latest models use context-awareness to track user preferences, history, and situation for hyper-personalization.

• Chain of Thought (CoT): Models articulate step-by-step reasoning, boosting performance on complex problem-solving.

• Fine-Tuning & Domain-Specific Intelligence: Training LLMs on custom datasets for specialized domains (healthcare, legal, financial), as general-purpose models increasingly allow domain adaptation.

• Multimodal & Omnichannel Interactions: 2025 chatbots handle text, speech, images, and gestures—users may interact seamlessly across platforms and input types.

• Emotion AI: Systems adjust responses based on detected user emotions, boosting engagement and satisfaction.

• Speech-to-Speech Assistants: Real-time, natural voice conversations are back in focus, with advanced speech recognition and synthesis.

• Open-Source AI & Developer-Led Innovation: Hugging Face, LLaMA 3, Mistral, etc., fuel rapid prototyping and agility in conversational agent creation.

• Autonomous Development & Co-Creation: AI is now a coding partner, helping build applications, interfaces, and creative content autonomously.

  
  

3. LLM Validation

• Human-in-the-Loop (HITL): Human reviewers verify/adjust AI outputs, especially critical for sensitive or high-stakes scenarios. HITL now integrates emotional cues and escalates seamlessly to human agents.

• LLM as a Judge: Language models are themselves used for qualitative assessment and benchmarking of other LLM outputs.

• Build Ground Truth: Maintaining validated datasets for benchmarking model accuracy remains essential, especially as synthetic data creation (by models themselves) becomes mainstream.

• Fact-Checking with Real-Time Data: Live fact-checking via internet-connected LLMs (e.g., Copilot, GPT-4V) mitigates hallucinations and enhances answer reliability.

• Ethical and Explainable AI: There is heavy emphasis on model transparency, fairness, and ethical usage. AI systems now incorporate explainability features, bias mitigation, and content watermarking to build trust and reliability.

• Secure and Watermarked Content: AI-generated responses are tagged for provenance, combating misinformation and maintaining brand integrity.