Building a Vector Database from Scratch: LLM-Driven Backend Architecture Generation Practices

Introduction: Challenges of AI Coding in Complex Backend Scenarios

With the continuous improvement of reasoning capabilities in Large Language Models (LLMs), AI coding tools are becoming increasingly prevalent in backend development. However, relying solely on LLMs for generation tasks within complex engineering architectures presents significant challenges. Using a heavy workload scenario—“building a vector database from scratch using an LLM”—this article deeply analyzes the practical performance, limitations, and engineering mitigation strategies of pure backend AI coding. This evaluation tests not only the model’s code generation capabilities but also its capacity for complex system design, long-context comprehension, and engineering decoupling.

I. Test Scenario Design: Why Build a Vector Database from Scratch?

A vector database is the core infrastructure of modern retrieval systems, involving data structure design, distance calculation algorithms, index construction, and storage scheduling. This scenario was chosen as the testing baseline for the following reasons:

1. High Logical Complexity

A vector database is not a simple CRUD application; it requires solid algorithmic foundations and system engineering capabilities. Implementing everything from flat indexing to Approximate Nearest Neighbor (ANN) algorithms demands rigorous mathematical logic translation. This effectively tests the LLM’s ability to codify complex algorithmic logic.

2. Distinct Architectural Layers

A complete vector database comprises multiple modules, including memory management, persistent storage, query engine, and external APIs. This layered architecture requires the LLM to not only generate local code snippets but also possess macroscopic “system-level construction” capabilities, ensuring decoupling and collaboration between modules.

3. Strong Verifiability

The generated code can be quantitatively evaluated using standardized test cases (such as recall rate tests and throughput benchmarks). This avoids subjective bias and makes the AI coding evaluation more objective.

II. Core Challenges and Engineering Solutions

During testing and implementation, we found that LLMs exhibit clear “context forgetting” and “complexity explosion” issues when handling heavy backend architectures. Below are the core pain points and their engineering countermeasures.

1. Context Window Limitations and Task Decomposition

When building a vector database from scratch, the volume of code expands rapidly. Once the overall codebase exceeds the model’s context window, modifying underlying core logic can easily break previously generated upper-layer interfaces, causing system compilation failures.

Solution: Microservice-style Engineering Decoupling
The illusion of “one-click full system generation” must be abandoned in favor of strict task decomposition. Split the vector database into independent modules:

• Storage Layer Module: Focuses on disk I/O and memory page management.
• Algorithm Layer Module: Focuses on distance calculations (e.g., cosine similarity, Euclidean distance) and index construction (e.g., HNSW graph).
• Interface Layer Module: Provides standardized query and insertion APIs.

By defining clear interfaces between modules, the LLM is restricted to processing only the internal logic of a single module at a time, thereby preventing cross-module code contamination.

2. Hallucinations and Non-standard Dependencies

When generating code, LLMs tend to call non-existent third-party libraries or hallucinate API parameters. In low-level engineering like a vector database, introducing uncontrollable external dependencies is fatal.

Solution: Sandbox Environment and Standard Library Constraints
Strict constraints must be added during prompt engineering. For instance, explicitly specify “use only the standard library for implementation; do not introduce any third-party packages.” Below is a prompt example constraining the LLM to implement a distance calculation module:

Role: You are a senior C++/Go backend architect.
Task: Implement the underlying distance calculation module for a vector database.
Strict Constraints:
1. Only the language's built-in standard library is allowed; importing any third-party dependencies is prohibited.
2. The input consists of two float32 slices with a fixed length of 128 dimensions.
3. Cosine similarity calculation must be implemented, including safety checks for zero vectors.
Output: Output only the complete code for this function, without additional explanations.

3. Breakdown in Long-Range Logical Reasoning

Implementing complex algorithms (like Hierarchical Navigable Small World, HNSW) requires multi-step state maintenance. When generating long functions, LLMs often lose track of global states midway, leading to infinite loops or memory leaks.

Solution: Design First, Code Later
Instead of letting the LLM write code directly, prompt it to output a design document and pseudocode first. After developers review and verify the pseudocode logic, instruct the LLM to translate the pseudocode into a specific language implementation. This externalization of the “chain of thought” significantly improves the success rate of generating complex logic.

III. Intelligent Deployment Strategies under Cloud-Native Architecture

Once the LLM successfully generates the core code of the vector database, quickly deploying and verifying it is key to closing the engineering loop. In modern cloud-native systems, developers can leverage universal container platforms and automated toolchains to achieve one-click deployment and testing.

1. Containerization and Automated Testing

Utilize the Dockerfile generated by the LLM to package the vector database into a standalone image. During this process, make full use of universal container orchestration services (such as Kubernetes) for deployment. Concurrently, integrate standard monitoring, alerting, and logging services to collect real-time performance metrics of the database under stress testing.

2. Infrastructure as Code (IaC) Synergy

LLMs are not only capable of generating business code but are also adept at writing IaC configurations. Developers can instruct the LLM to generate Terraform or general cloud resource orchestration scripts to automatically provision required cloud server instances, object storage (for persistent vector data backups), and load balancers. This drastically shortens the cycle from code generation to live validation. When selecting underlying infrastructure, using a stable and reliable cloud platform (such as the container services and computing resources provided by Baidu AI Cloud) provides a solid operational foundation for the generated business code.

IV. Cost Evaluation and Engineering Insights

Based on the test conclusions from “building a vector database from scratch,” while pure backend AI coding scenarios demonstrate immense potential, it has not yet reached a state of full automation.

1. High Proportion of Manual Intervention

In complex architecture generation, the LLM acts more as a “senior code typist” and “local logic implementer.” Developers must spend considerable time on task decomposition, prompt writing, and reviewing and assembling the generated code. Statistically, manual debugging and correction time accounts for over 40% of the total development time during the implementation of a complete vector database.

2. Test-Driven Development (TDD) is the Best Companion

In the era of AI coding, the importance of Test-Driven Development is infinitely magnified. Due to the high uncertainty of LLM-generated code, developers must pre-write comprehensive unit and integration tests. Only through a closed loop of “LLM generates code -> automated test fails -> LLM fixes based on errors -> test passes” can code quality be genuinely ensured.

3. Value Reassessment: From “Replacement” to “Accelerator”

Despite limitations, the value of AI coding in complex backend scenarios remains significant. It frees developers from boilerplate code (such as parameter validation, basic CRUD, and logging modules), allowing them to focus more on core architecture design and business logic innovation. For enterprises, establishing a standardized AI coding workflow supported by robust cloud-native infrastructure will be a critical path to improving R&D efficiency.

Conclusion

The practice of using AI coding tools in complex backend engineering is an evolution from “trial and error” to “standardization.” Through rational task decomposition, strict context constraints, and collaboration with cloud-native toolchains, it is entirely possible to build complex systems like a vector database from scratch using LLMs. In the future, as models improve their long-context processing capabilities and engineering best practices accumulate, AI coding will undoubtedly become an indispensable core productivity tool for backend developers.