Building My First RAG System: Deriving the Architecture from First Principles - Part One

The author is trying to solve a memory and organization problem. Useful information lives in separate tools, so finding it later can be difficult. A personal retrieval-augmented generation system creates a common search layer over those sources. This matters because stored knowledge is valuable only when it can be found and used.

The article gives a clear example. A VC copied online material into a NotebookLM repository whenever he wanted to remember it. Later, when writing a blog post, he queried the repository and retrieved the information he needed. The key mechanism is retrieval: the system locates relevant passages before generating an answer.

The author’s own resources are scattered across Logseq, Gmail, Notion, ADR documents, and Slack. The source does not describe a completed system or measure its performance. Still, the direction is clear: connecting these sources could turn fragmented records into a practical, queryable personal knowledge base.

Retrieval-augmented generation, or RAG, combines search with language generation. A system first finds documents or passages related to a question. It then places those results in the prompt given to a language model. This matters because the model can use current, private, or specialized information without having memorized it during training.

For example, a question about a past project could search connected Logseq notes, Gmail messages, Notion pages, Slack conversations, and ADR documents. The retriever selects matching text. The language model then uses that text to compose an answer. The knowledge base remains separate from the model’s general training.

RAG does not guarantee truth. Poor indexing, missing documents, or irrelevant retrieval can still produce weak answers. However, it offers a practical way to query personal information. The source’s NotebookLM example illustrates this basic pattern: save material, retrieve it later, and use it for writing.

A personal RAG system can connect information sources that contain useful personal or work knowledge. These sources may include note-taking tools, email, documentation, project records, and communication platforms. Connecting them matters because important context is often divided between systems rather than stored in one library.

The article names Logseq, Gmail, Notion, ADR documents, and Slack as examples of scattered resources. Logseq and Notion may hold notes. Gmail may contain correspondence. ADR documents record architecture decisions. Slack may preserve discussions and decisions. A connector would collect accessible content, prepare it for search, and return relevant sections when asked.

The source does not specify which integrations already exist or how permissions would work. In practice, access controls, synchronization, duplicate content, and privacy are important. A useful system must search across these sources while respecting their original boundaries and keeping changing information updated.

A personal RAG system can realistically index a large collection of notes, messages, documents, and saved articles. There is no single capacity number because the limit depends on storage, processing power, search technology, and the size of each document. The source article does not state how much information the author’s system contains.

As the collection grows, documents are usually divided into smaller passages and represented in a searchable index. A question is compared with those passages, and only the most relevant results are sent to the language model. This keeps each answer’s context manageable, even when the overall archive is much larger.

Growth creates practical challenges. Duplicate or outdated material can confuse retrieval. Permissions and source synchronization also matter. A larger archive is not automatically better. Good chunking, metadata, filtering, and ranking help the system find the right evidence. The main constraint is usually retrieval quality and maintenance, not simply the number of stored documents.

When a system retrieves relevant documents first, the language model receives targeted context before composing its response. This changes the task from recalling general patterns to using evidence supplied for the specific question. It matters especially for private information that the model could not know from public training data.

Suppose someone asks for ideas for a blog post based on saved reading. The system searches the repository and returns related passages. The model can then summarize those passages, connect their ideas, or use them to draft an outline. This mirrors the article’s example of querying a repository when writing a blog post.

Retrieval improves relevance, but it is not a guarantee of accuracy. If the wrong documents are retrieved, the answer may still be incomplete or misleading. The system may also need citations or links so the user can check the evidence. Still, retrieval gives responses a direct connection to the user’s stored material.

RAG is often preferable when information changes frequently because the source documents can be updated independently of the language model. New or revised content is added to the index, allowing future questions to retrieve it. This avoids repeatedly changing the model’s internal parameters through retraining or fine-tuning.

For example, a new Slack decision, Gmail message, or architecture document could be ingested into the knowledge base. A later question could retrieve that material immediately after indexing. The model would then use the updated passage while generating its answer. The article’s collection of scattered, evolving resources makes this separation useful.

RAG does require maintenance. Connectors must synchronize sources, indexes must be refreshed, and access permissions must remain correct. Fine-tuning can still help with style or specialized behavior, but it is usually less convenient for frequently changing facts. The source does not compare costs directly; this explanation follows established RAG practice.

An embedding is a list of numbers produced by a machine-learning model for text. The numbers encode useful patterns learned from language, such as related concepts and common contexts. Texts with similar meanings tend to receive vectors that are close together under a mathematical similarity measure. This lets a system search by meaning, not just matching words.

A knowledge-base pipeline might split an article into passages and create an embedding for each one. It also embeds the user’s question. The system compares the question vector with passage vectors, often using cosine similarity, and returns the closest matches. A question about architecture choices could therefore retrieve an ADR using different wording.

Embeddings are useful but imperfect. Ambiguous text, poor passage boundaries, and missing context can reduce retrieval quality. Systems often combine vector search with keyword search, filters, and reranking. The source article does not explain embeddings directly; this description supplies established background for how RAG search commonly works.