Difference Between Completions and Embeddings in AI Models

Artificial Intelligence (AI) models have rapidly evolved to perform various tasks ranging from text generation to natural language understanding. Among the different types of AI-driven models, Completions and Embeddings serve distinct purposes. While they both utilize machine learning techniques, their applications and functionality differ significantly. In this article, we’ll explore the differences between completions and embeddings, their use cases, and how they contribute to AI applications.

What are Completion Models?

A completion model refers to an AI system that generates text based on an input prompt. These models predict and produce a sequence of words or phrases that logically follow a given text. They are often built using deep learning techniques, particularly large language models (LLMs) such as OpenAI’s GPT (Generative Pre-trained Transformer).

How Completions Work

Completion models use probabilistic methods to predict the next token (word or character) in a sequence based on context. They leverage:

• Pre-trained knowledge: Models are trained on vast datasets containing text from books, websites, and other sources.
• Contextual understanding: The model interprets the structure and semantics of the input text to produce coherent and relevant responses.
• Token prediction: Using statistical probabilities, the model determines the most likely next word or phrase in the sequence.

Applications of Completion Models

1. Text Generation / Generative AI – Used for writing articles, summaries, or responses in chatbots.
2. Code Autocompletion – Helps developers by suggesting or completing code snippets.
3. Conversational AI – Powers virtual assistants and customer service bots.
4. Storytelling and Creative Writing – Generates narratives, poems, and scripts.

Example of a Completion Model Output

Input Prompt:

The future of artificial intelligence is

Completion Response:

...poised to revolutionize industries such as healthcare, finance, and education by automating complex tasks and enhancing decision-making.

What are Embedding Models?

An embedding model is an AI system that transforms words, sentences, or documents into numerical vector representations. These vectors capture the semantic meaning and relationships between textual data points, enabling efficient comparisons and retrieval of information.

How Embeddings Work

Embedding models convert textual input into dense numerical vectors in a high-dimensional space. These vectors are structured so that semantically similar words or phrases are closer together. The process involves:

• Tokenization: Breaking text into words or subwords.
• Vectorization: Mapping words to numerical representations using pre-trained embeddings (e.g., Word2Vec, GloVe, BERT-based embeddings).
• Dimensionality Reduction: Compressing data while preserving essential features.

Applications of Embedding Models

1. Search and Information Retrieval – Improves search engine relevancy by finding contextually similar documents.
2. Recommendation Systems – Suggests content based on semantic similarity (e.g., movie or product recommendations).
3. Sentiment Analysis – Helps understand emotions and opinions in text data.
4. Text Clustering and Classification – Groups similar documents or categorizes content based on meaning.

Example of an Embedding Model Output

If we use an embedding model to analyze the words “king” and “queen,” their numerical representations (vectors) may appear as follows:

king → [0.12, -0.45, 0.87, …]
queen → [0.15, -0.50, 0.82, …]

Since their vectors are close in space, the model recognizes them as related concepts.

Key Differences Between Completions and Embeddings

When to Use Each Model

• Use completions when you need text generated dynamically based on a prompt.
• Use embeddings when you need semantic understanding, such as searching for similar documents or classifying text.

Sometimes, these models complement each other. For instance, embeddings can first find relevant documents, and a completion model can then summarize or extract insights from them.

Using Completions and Embeddings Together in a Generative AI System with RAG

Retrieval-Augmented Generation (RAG) is a design pattern that enhances text generation by leveraging both embeddings and completion models. It integrates information retrieval techniques with generative AI to provide more accurate and contextually relevant responses.

One of the major benefits of RAG is that it allows generative models to incorporate real-time or domain-specific knowledge without requiring extensive retraining. Unlike standalone language models that rely on static pre-training data, RAG can dynamically retrieve relevant content, ensuring that responses are up-to-date and contextually accurate. This reduces hallucinations—where AI generates plausible but incorrect information—by grounding outputs in factual data.

Additionally, RAG enhances explainability and trustworthiness in AI systems. Since retrieved documents serve as a basis for generated content, users and developers can trace responses back to their sources, improving transparency. This is particularly valuable in high-stakes domains such as legal, medical, and financial applications where accuracy is critical.

Steps in RAG:

1. Retrieval with Embeddings – When a query is received, an embedding model converts the input into a vector representation and searches a knowledge base for the most relevant documents.
2. Augmentation of Context – The retrieved documents or snippets are added as additional context to the prompt for a completion model.
3. Completion-based Response Generation – A completion model processes the augmented input and generates a coherent and informed response.

Applications of RAG in AI Systems

• Question-Answering Systems – Improves chatbot and virtual assistant responses by retrieving factual knowledge before generating text.
• Enterprise Search – Enhances internal search systems by retrieving relevant documents and summarizing them dynamically.
• Legal and Medical Research – Assists professionals by retrieving relevant case studies or research papers and summarizing findings in natural language.
• Content Creation – Generates informed articles and reports by integrating real-time knowledge retrieval.

By combining the strengths of embeddings for knowledge retrieval and completions for natural language generation, RAG-based AI systems can provide more accurate, contextually aware, and insightful responses. This hybrid approach significantly enhances AI-driven applications across various industries.

Conclusion

Completions and embeddings are two foundational AI model types that, when used together, significantly enhance the capabilities of generative AI systems. Embeddings enable AI to understand and retrieve semantically relevant content, while completions provide fluent and context-aware text generation. Through design patterns like RAG, organizations can create AI applications that are more accurate, efficient, and contextually rich. As AI technology continues to evolve, integrating retrieval-based methods with generative models will be essential for improving trust, reliability, and real-world applicability across multiple industries.

Original Article Source: Difference Between Completions and Embeddings in AI Models written by Chris Pietschmann (If you're reading this somewhere other than Build5Nines.com, it was republished without permission.)