Introduction
Generative AI (GenAI) is reshaping how we create, analyze, and interact with content. Whether you’re a developer, product manager, data scientist, or simply curious about the field, understanding the key concepts behind GenAI is crucial. In this post, we’ll break down 12 foundational terms—what they mean, why they matter, and how they fit into the broader GenAI landscape.
1. LLM (Large Language Model)
What it is:
An LLM is a deep neural network trained on massive text corpora (e.g., Common Crawl, Wikipedia).
Why it matters:
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Serves as the backbone for chatbots, summarization tools, and more.
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Exhibits zero-shot and few-shot learning capabilities.
2. Transformers (Transformer Architecture)
What it is:
A neural network design using self-attention mechanisms to weigh input tokens relative to one another.
Why it matters:
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Enables parallel processing of sequence data (text, code).
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Powers modern LLMs like GPT, BERT, and others.
3. Prompt Engineering (AI Instruction Design)
What it is:
The craft of designing input “prompts” (instructions, context, constraints) to guide a GenAI model toward desired outputs.
Why it matters:
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Small wording changes can vastly improve output relevance and accuracy.
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Critical for applications where precision and reliability matter (e.g., legal, medical).
4. Fine-tuning (Model Specialization)
What it is:
Adapting a pre-trained AI model to a specific domain or task by continuing training on a smaller, specialized dataset.
Why it matters:
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Boosts performance for niche use-cases (e.g., domain-specific customer support).
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Often more cost-effective than training from scratch.
5. Embeddings (Vector Representations)
What it is:
Numeric vectors that encode the semantic meaning of text, images, or other data in high-dimensional space.
Why it matters:
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Underpins semantic search, recommendation engines, and similarity matching.
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Allows efficient retrieval and clustering based on “meaning” rather than keywords.
6. RAG (Retrieval-Augmented Generation)
What it is:
A hybrid approach combining information retrieval (from documents, databases, etc.) with generative models to produce factual, context-aware responses.
Why it matters:
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Addresses hallucination by grounding generation in real sources.
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Ideal for knowledge-intensive tasks like Q&A systems and report generation.
7. Tokens (Text Units)
What it is:
The smallest discrete units of text (words, subwords, or characters) that a model processes.
Why it matters:
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Defines model input length and computational cost.
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Tokenization strategy affects model performance and output clarity.
8. Hallucination (AI Fabrication)
What it is:
When a GenAI model generates plausible but factually incorrect or fabricated information.
Why it matters:
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A key reliability challenge—especially critical in high-stakes domains.
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Mitigated through techniques like RAG and rigorous prompt design.
9. Zero-shot (Zero-shot Learning)
What it is:
A model’s ability to tackle new tasks without any explicit examples—relying solely on its pre-training knowledge.
Why it matters:
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Enables rapid prototyping of new features without collecting labeled data.
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Demonstrates the broad generalization power of large models.
10. Chain-of-Thought (Reasoning Process)
What it is:
A prompting technique that encourages the model to break down complex problems into sequential reasoning steps.
Why it matters:
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Improves accuracy on tasks requiring multi-step logic (e.g., math word problems).
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Enhances explainability by surfacing the model’s “thought process.”
11. Context Window (Input Capacity)
What it is:
The maximum number of tokens a model can consider in a single pass.
Why it matters:
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Limits how much conversation or document history can inform the response.
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New “long-context” models push this boundary, enabling book-length inputs.
12. Temperature (Randomness Parameter)
What it is:
A control knob (usually between 0 and 1) that adjusts the randomness of model outputs. Lower values make outputs deterministic; higher values increase creativity.
Why it matters:
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Balances consistency vs. originality depending on your application needs.
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Tuning temperature helps avoid overly repetitive or nonsensical responses.
Conclusion
These 12 terms form the core vocabulary of today’s GenAI landscape. Mastering them will help you:
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Design better prompts that yield accurate, reliable outputs.
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Choose the right techniques (e.g., RAG vs. fine-tuning) for your use-case.
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Understand model behavior, limitations, and how to mitigate risks like hallucination.
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