rag context engineering
Retrieval-Augmented Generation: Context, Vector Databases, and Engineering
In the domain of Retrieval-Augmented Generation (RAG), context plays a central role in determining the effectiveness, accuracy, and relevance of large language model (LLM) outputs. RAG is an architectural pattern that combines traditional generative AI models with external knowledge retrieval systems, and it is designed to overcome the inherent limitations of static LLMs. To understand how RAG systems operate effectively, we must examine the data pipeline, the role of vector databases, and the practice of context engineering—each of which plays a pivotal role in grounding LLM responses in reliable, external knowledge.
1. The RAG Data Pipeline
The RAG data pipeline is a structured sequence of steps used to prepare and integrate external data for retrieval. It begins with data ingestion and preprocessing, where raw documents—web pages, PDFs, articles—are cleaned, tokenized, and normalized. These documents are then split into manageable “chunks” using semantic or fixed-length chunking strategies to improve retrieval granularity. Metadata, such as titles or timestamps, is added to each chunk.
Next, these chunks are converted into vector embeddings using a semantic encoder like BERT, Ada, or SentenceTransformer. The embeddings, along with metadata, are indexed in a vector database, allowing for similarity-based retrieval using cosine or L2 distance. At runtime, when a user submits a query, it is embedded into a vector and compared against the stored vectors to retrieve the most relevant chunks. These chunks are then passed into the LLM’s context window as part of a carefully constructed prompt.
2. PostgreSQL’s Role in RAG
Traditionally, vector databases such as FAISS or Pinecone are used for similarity search in RAG, but PostgreSQL has recently gained powerful features that allow it to participate in this workflow. The pgvector extension enables PostgreSQL to store and search high-dimensional vectors using similarity metrics like cosine similarity and inner product. This transforms PostgreSQL into a viable vector store, making it possible to unify metadata filtering, vector search, and document storage in a single SQL-native system.
In addition, PostgreSQL supports full-text search (tsvector), which can be combined with vector search to build hybrid retrieval systems. Features like JSONB fields allow for complex metadata filtering, and triggers/materialized views make it easier to automate updates when the underlying data changes. These features are critical for enterprise-grade RAG systems where document lifecycles, access control, and low-latency querying are required.
3. Vector Databases and the Context Problem
One of the fundamental limitations of LLMs is their fixed context window, which constrains how much information they can attend to at once. Moreover, LLMs are stateless—they don’t know anything outside what’s in the prompt unless memory is managed externally. This is where vector databases are essential: they act as semantic memory layers, retrieving the most relevant content on-demand, based on the meaning of the query.
By storing document embeddings, vector databases allow for semantic search rather than keyword matching. This means the system can fetch chunks of text that are topically relevant, even if they don’t share exact wording with the user query. As a result, the LLM is given a precise, compressed version of relevant context that improves factual grounding, reduces hallucinations, and ensures responses are specific to the domain or knowledge base intended.
4. Context Engineering
Context engineering refers to the deliberate design of the input prompt and retrieval strategy to maximize output quality. It includes prompt formatting, choosing which retrieved documents to include, summarizing content to fit within token limits, injecting metadata, and structuring multi-turn conversations with sliding windows or memory buffers. A well-engineered context might include a few top-ranked document chunks, formatted with markdown or XML tags to help the LLM parse structure and intent.
Critically, context engineering also includes decisions about how to filter irrelevant or conflicting content, how to balance general knowledge with domain-specific facts, and how to prompt the model in a way that aligns with the use case—be it answering a legal question, summarizing financial news, or assisting with coding.
Conclusion: Critical Issues and Summary
The success of RAG systems depends on the intersection of high-quality retrieval, efficient context management, and intelligent prompt engineering. Key issues include:
- Context Limitations: LLMs are bounded by token windows, so irrelevant or poorly ordered context can degrade performance.
- Retrieval Precision: Vector search must return highly relevant results; otherwise, even good prompts fail.
- System Integration: PostgreSQL’s pgvector and hybrid query capabilities now allow developers to build complete, auditable RAG pipelines within a unified stack.
- Prompt Engineering: Without thoughtful design of the input structure, the model will not reliably ground its outputs—even with good retrieval.
RAG is not just a technical pattern—it is a system-level design philosophy that recognizes the limitations of generative models and compensates for them with curated knowledge and careful context control.