Design, simulate, and evaluate production-grade Retrieval-Augmented Generation architectures built for enterprise-scale AI platforms.
Why modern enterprise AI requires systems-level architecture instead of standard developer demos.
Click on any node in the architectural flow below to inspect its purpose, challenges, and lessons.
Select an architectural block to view details.
Every critical architectural block covered inside the Enterprise RAG System Designer lessons.
Build parsing pipelines that retain table formats, layouts, headers, and hierarchical text nodes.
Benchmark Recursive splits vs Semantic Chunking to preserve contextual boundaries.
Explore dimension properties, custom token filters, and embedding models optimization.
Configure HNSW, IVF index quantization, and optimize queries latency for millions of items.
Combine dense vector search with keyword-based BM25 using Reciprocal Rank Fusion.
Use Cross-Encoder architectures to filter out irrelevant contexts and boost Top-K precision.
Enforce strict citations formatting, mapping outputs directly back to source documents.
Implement post-generation guardrails checking for unsupported semantic assertions.
Configure automated diagnostic metrics: Faithfulness, Answer Relevance, and Recall.
Trace prompts, token limits, context values, and latency spans across all requests.
Use semantic cache strategies and prompt compression to reduce costs by 40-70%.
Implement metadata partition filters to isolate clients data in multi-tenant environments.
Configure variables to see estimated latency, precision, monthly cost, and safety scores.
Automated evaluation scores updated dynamically based on sandbox configuration.
Measures if the generated answer is derived exclusively from the retrieved contexts.
Measures if the response addresses the query directly without tangential bloat.
Measures whether retrieved context matches exact relevance expectations.
Measures the ratio of gold-standard references found in retrieved chunks.
Factual compliance checking, detecting claims made without source proof.
Validates what percentage of output assertions are linked to a citation.
Critical considerations when moving RAG designs into active client-facing environments.
Deduplicating LLM inferences by checking user query cosine similarity against database caches, reducing api latency & overall transaction costs.
Processing thousands of documents asynchronously using brokers (e.g. RabbitMQ, Kafka) so parser bottlenecks do not drop payloads.
Grouping text nodes into batches before sending to LLM/embedding servers to handle network timeouts and optimize token billing limits.
Scaling database index storage partitions across multiple server instances to guarantee sub-10ms similarity queries under high volume traffic.
Injecting absolute tenant ID keys at query time to prevent information leaks and ensure secure data partitioning across organizations.
Continuous monitoring of request-level token spend, model latency breakdowns, and caching rate logs for infrastructure cost controls.
Following DevJam's architectural blueprint for shipping production search systems.
Interactive playground layout explorer, design tokens, and parameter-based simulator metrics calculations.
Interactive interface to upload user files (.pdf, .txt) and view visual overlays comparing recursive split structures.
Running local inference using sentence-transformers, storage inside Qdrant Vector database, and basic indexing configuration.
Adding BM25 sparse index layers and reranking pipelines to maximize Top-K search relevance and precision outputs.
Integrating hallucination validators, post-generation checks, and citation mappings using framework evaluations (Ragas).
Instrumenting spans with OpenTelemetry, adding semantic cache configs, latency logs, and monitoring charts.
DevJam is a developer community roadmap project built by engineers, for engineers. We welcome code contributions, documentation, and reviews from developers passionate about AI search infrastructure.