Vector Databases — from Search to RAG: My Learning Experience at asibiont.com

By 2026, vector databases have ceased to be exotic and have become core infrastructure for any AI application. They are what allows RAG (Retrieval-Augmented Generation) systems to quickly find relevant knowledge fragments for language models. However, until recently, I, like many developers, used vector databases as a black box without understanding the internal mechanics. The decision to take the course "Vector Databases — from Search to RAG" on the ASI Biont platform became a turning point — I not only filled the gaps in my knowledge but also successfully implemented a RAG pipeline in a commercial project.

Why is this an important topic? Generative AI continues to evolve, and by 2026 almost every company working with NLP uses RAG to improve the accuracy and relevance of LLM answers. Vector databases are the core of such systems. Understanding how similarity search works, how to choose the right tool and scale it is a key skill for an engineer aspiring to the role of RAG engineer or AI architect. In this article, I will share my experience of taking the course, break down its content, and also show how the knowledge gained helped me solve a real problem.

What are vector databases and why are they necessary

Vector databases store data in the form of numerical vectors — embeddings, which are obtained from text, images, and audio using machine learning models. Search in such databases is based not on exact matches but on the distance between vectors, which makes it possible to find semantically similar objects. For example, a query "how to get a refund for an undelivered product" will find the document "Refund Policy" even if it contains none of the words from the query. It is this property that makes vector databases indispensable for RAG.

The rise of vector databases is a natural stage in the evolution of data storage systems. By 2026, they have become the standard for recommendation systems, image search, anti-fraud, and answer generation with external knowledge. Many analysts link their growth to the popularity of RAG patterns described in the research paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" (Lewis et al., 2020, arXiv:2005.11401).

How I chose a course on vector databases

When I decided to systematically study the topic, I ran into a problem: most materials on the internet were either superficial retellings of documentation or giant academic courses far from practice. I was looking for a program that would cover all stages: from the mathematics of embeddings and similarity search to advanced ANN algorithms and integration with RAG frameworks. My choice fell on the course "Vector Databases — from Search to RAG" on the ASI Biont platform. What attracted me?

1. Completeness of coverage — the program covered all key topics: tool comparison, ANN algorithms (HNSW, IVF, PQ, DiskANN), hybrid search, multi-tenancy, scaling, and even the economics of managed vs self-hosted.
2. Practical focus — each module comes with working code and comparison tables, allowing you to apply the knowledge immediately.
3. Learning format — text lessons with AI-generated personal curriculum, available 24/7.

Course program: from theory to practice

The course is structured as a journey from fundamental concepts to complex engineering. I completed it in six weeks, spending 5–7 hours per week. Below are the main blocks and their value.

Embedding mathematics and similarity search

The first module lays the foundation: what embeddings are, how models convert text into vectors, how cosine similarity differs from Euclidean distance and L2 norm. Here I truly understood for the first time why cosine similarity is more often used for text data: it is invariant to vector scale and better models semantic similarity. The course provides clear examples with Python calculations that can be run directly on the platform.

A big plus is the examination of the HNSW method at an intuitive level: how hierarchical graphs are built, why searching from the upper layers to the lower ones gives logarithmic complexity. This allowed me to consciously choose index parameters (M, efConstruction) for a specific task later on.

Tool overview: comparison table

One of the most useful sections of the course is the comparative analysis of seven vector databases: Pinecone, Weaviate, Milvus, Qdrant, pgvector, Chroma, LanceDB. Instead of abstract descriptions, the author provides tables showing for each tool the supported algorithms, license, deployment method, and whether it is suitable for production. Here is a condensed version I put together after completing the course:

This table became my cheat sheet when choosing a tool at work.

ANN algorithms: HNSW, IVF, PQ, DiskANN

Understanding approximate nearest neighbor (ANN) algorithms is what distinguishes an engineer from a user. The course covers in detail:

• HNSW — a graph structure of a hierarchical type that provides an excellent balance between speed and accuracy. It is the default algorithm in many databases.
• IVF (Inverted File Index) — clustering by centroids, speeds up search but requires prior training.
• PQ (Product Quantization) — vector compression, saves memory but may reduce accuracy.
• DiskANN — an algorithm for working with data larger than RAM, uses SSD. Ideal for ultra-large collections.

The most valuable part was practical exercises: I built indexes in Qdrant and changed parameters, observing how latency and recall changed. These skills were directly useful when configuring a production system.

Hybrid search and multi-tenancy

Hybrid search combines vector similarity with keyword search (BM25). This solution saves cases where the semantic model struggles with rare terms or abbreviations. The course shows how to implement hybrid search in Qdrant and Weaviate, and how to rank results. I learned that many production systems (e.g., documentation search) use a hybrid approach for maximum relevance.

Multi-tenancy is the ability to separate data of different clients in one database. In vector databases, this is implemented through partition keys. In the course, I learned how to set up such partitions to prevent data leakage between tenants, and understood why this is critically important for B2B products.

Scaling and production

This section covers sharding and replication in detail. I figured out how data is distributed across nodes, how index rebuilding works, and how to choose the number of replicas for reads. The economics topic is also covered separately: when it is beneficial to use a managed service (e.g., Pinecone, Supabase) and when to deploy self-hosted Qdrant or Milvus. The course provides an interesting cost comparison for different data volumes and query numbers.

During practice, I independently deployed a Milvus cluster in a test environment and tested performance under horizontal scaling. This was an invaluable experience that later helped me design infrastructure for a project with several million documents.

Integration with RAG: LangChain and LlamaIndex

The culmination of the course is a module on integrating vector databases into a RAG pipeline. Here we worked with LangChain and LlamaIndex. I learned to build chains: document loading → splitting → embeddings → writing to vector database → retrieval → passing context to the LLM and generating a response.

The course provides not only ready-made snippets but also error analysis. For example, how to correctly choose the chunk size and overlap so that retrieval is accurate. This immediately improved the quality of our chatbot's responses.

How learning on asibiont.com works

All courses on the asibiont.com platform are completely text-based. At first glance, this may seem unusual, but this format has a serious advantage: focus on the essence without distracting video. You get structured material that can be quickly read, re-read, and summarized. I, for example, prefer to learn at my own pace, and the absence of video lectures meant I didn't have to rewind or pause recordings.

The key feature of asibiont.com is AI-generated personalized lessons. At the start, the system asks questions about your knowledge level and learning goals. Based on the answers, the neural network generates a program for a specific student. For example, if you are already familiar with Python basics but don't know what embeddings are, the first lessons will be specifically about embeddings, while basic language constructs will be skipped.

Unlike recorded courses that become outdated over time, the content here is constantly updated. The AI model takes into account the latest changes in libraries and new tools that emerge. For me, as a practitioner, this is a huge plus — I was confident that I was getting up-to-date information.

I also liked that there are test assignments at the end of each block. They allow the system to adapt the subsequent program: if you answer incorrectly, additional explanations and simpler explanations follow. If correctly, the course accelerates so you don't get bored. This is an effective feedback loop that works, not just decoration.

Why AI learning is modern and effective

Traditional courses, even good ones, approach the learning process in an averaged way. All students follow the same program, and the teacher physically cannot adapt to each one. Tests and homework partially handle this, but they do not provide flexible real-time adjustment. AI-generated lessons solve this problem in a new way.

First, the neural network adjusts the program to the student's level. If you are a beginner, you get more explanations, examples, and references. If you are an experienced developer, the pace will be higher and the focus on advanced details. I, for example, already had experience with pgvector and skipped part of the introductory material; I was immediately directed to the comparison of ANN algorithms.

Second, AI explains complex topics in simple language. Technologies do not stand still, and much documentation is written by engineers for engineers. AI can digest technical complexity, break it down, and find analogies. Surprisingly, the explanations in the course were clearer than in Pinecone's official tutorials.

Third, AI assistants on the platform keep content up to date. Every time I opened a lesson, I did not see outdated recommendations. This is critical for the rapidly changing field of vector databases, where new algorithms appear every year.

Finally, 24/7 access means you can study at any time, at a convenient pace. This is especially important for people who combine learning with work. I often studied on Sunday evenings and on business trips.

Applying the knowledge: my case

Now a few words about the practical result. In the middle of the course, I was working on a project to create a chatbot for technical support. The company had accumulated a lot of documentation in Confluence, and users often asked the same questions. Previously, we used keyword search, but it performed poorly: synonyms, different phrasings, typos — all led to incorrect answers.

After completing the module on Qdrant, I decided to implement a RAG system. I wrote a Python script: extracted all documents, split them into chunks of 500 tokens with an overlap of 100 tokens, generated embeddings using the sentence-transformers model (all-MiniLM-L6-v2), and loaded them into Qdrant, configuring an HNSW index with the parameters from the course. Then I connected OpenAI GPT-4 for answer generation.

The result exceeded expectations:
- The time to find a relevant document dropped from 5–7 seconds to 200 milliseconds.
- The percentage of successful answers on the test set rose from about 45% to 80%.
- The load on the support team noticeably decreased, as the chatbot began to respond more accurately.

Thanks to the course, I knew not only how to do something, but also why it works. Later, when the solution needed to be scaled, I confidently chose Qdrant in self-hosted mode, added replicas, and ran load tests — all of this was covered in the course program.

Conclusions

The course "Vector Databases — from Search to RAG" on asibiont.com is not just a collection of lectures. It is a complete learning system in which theory is backed by code, and knowledge is reinforced through practical tasks. I went from not understanding how to benefit from vector representations to designing a production solution for large documentation.

What I learned during the course:
- Consciously choose a vector database for specific tasks.
- Configure ANN algorithms (HNSW, IVF, PQ) for an optimal speed/accuracy balance.
- Implement hybrid search and multi-tenant schemes.
- Scale vector databases using sharding and replication.
- Build RAG pipelines with LangChain and LlamaIndex.
- Understand the economic aspects of managed vs self-hosted.

I recommend this course to developers, data engineers, and AI enthusiasts who want to get an in-demand profession of RAG engineer and apply modern technologies in real projects. Learning on asibiont.com is truly a modern approach that helps you master a complex field in a short time without unnecessary fluff. If you are ready to go beyond a superficial acquaintance with vector databases, start learning right now.

Vector Databases — from Search to RAG