Generative modeling

As explained in Just Use Postgres!

Generative AI comprises artificial-intelligence models that produce new content such as text, images, audio, or code from learned patterns.

Generative artificial intelligence uses models trained on large datasets to produce new text, images, videos, audio, code, and other content from learned patterns. Large language models interpret natural-language prompts to answer questions, converse, and perform tasks. section 8.0 section 8.1.1

A generative AI application can:

• Accept natural-language prompts to search movie descriptions through stored vector embeddings. section 8.1
• Use an LLM as a conversational movie chatbot or an agent that manages watchlists. section 8.1
• Combine Postgres, pgvector, similarity search, and retrieval-augmented generation for personalized recommendations. section 8.7

Movie recommendation prompt

A user asks, “What are the other two movies produced by the studio that made Shrek?” An LLM can interpret the request and respond with examples such as “Madagascar” and “Kung Fu Panda.” section 8.1.1

Figure 8.1 The LLM uses various tools to perform tasks following user prompts.

LLMs can call web tools, services, protocols, and APIs when completing prompts that require actions beyond their internal knowledge. section 8.1.1

How Just Use Postgres! covers it

How deep it goes

Explains models, prompts, embeddings, tools, and application patterns without descending into model internals.

Examples

Uses movie recommendations, a Shrek filmography question, conversational chatbots, embeddings, and watchlist management.

What you can do after

Build it from scratch by combining LLMs, embeddings, Postgres, pgvector, similarity search, and RAG.

How it connects

Connects LLMs with Postgres, pgvector, vector embeddings, similarity search, retrieval-augmented generation, and AI agents.

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Related concepts

• language modelincludes · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• Gaussian mixture modelincludes · A Gaussian mixture model is a probabilistic model representing a distribution as a weighted combination of multiple Gaussian component distributions.
• socio-technical systempart of · A socio-technical system is an interdependent arrangement of people, technology, processes, and social or organizational structures that interact to achieve outcomes.
• artificial intelligencekind of · Artificial intelligence is the field concerned with creating computational systems that perform tasks associated with human intelligence, such as learning, reasoning, perception, or decision-making.
• variational autoencoderincludes · A variational autoencoder is a generative neural network that learns a probabilistic latent representation of data and decodes samples from it to reconstruct or generate data.
• generative adversarial networkincludes · A generative adversarial network is a generative model with a generator and discriminator trained in competition, enabling the generator to produce samples resembling a target dataset or distribution.

This explainer was generated by Manning's knowledge graph from the text of Just Use Postgres!. The section links point to the parts of the book it draws on.