RAG & AI Backend Engineering
I treat AI retrieval as a backend security and data-quality problem, with model choice only one part of the system.
What does this work deliver?
A retrieval application that can explain where an answer came from and which dataset it was permitted to use.
See the implementation in Multilingual multi-tenant RAG.
What problems does this solve?
A chat interface can generate plausible answers while retrieving irrelevant context—or context belonging to another customer. A dependable retrieval system must distinguish ingestion, identity, embedding compatibility and answer verification.
Good fit for
- Teams exploring document question-answering
- Products serving multiple customer workspaces
- Developers connecting embeddings and LLMs to existing APIs
1. Map documents, permissions and retrieval goals
I begin with who owns each document, which queries should succeed and which should be declined. Chunking strategy and metadata decide what can be found. Authentication, authorization and retrieval filtering must agree on the same tenant scope.
Engineering checks
- Define ingestion and source attribution
- Test unauthorized cross-tenant queries
- Create questions with no supported answer
2. Design embedding and generation stages independently
An embedding pipeline can run locally while generation calls a separate provider. For multilingual retrieval, the encoder and search query prefixes must match the selected model. I version embedding dimensions and prevent accidental comparisons across incompatible models.
Engineering checks
- Document model and vector dimensions
- Validate top-k retrieval against examples
- Set thresholds and refusal behavior
3. Make the service measurable and maintainable
I isolate API keys and rate-limit usage, record useful evaluation signals and plan for provider outages or bad source data. I do not call a working prototype production hardened without load, authorization and incident testing.
Engineering checks
- Record retrieval quality cases
- Test provider errors and quota limits
- Plan index rebuilds after model changes
Expected result and engineering trade-off
A retrieval application that can explain where an answer came from and which dataset it was permitted to use.
Retrieval correctness and authorization are more important than elaborate prompting or a larger language model.
Have a backend, data, or product challenge?
Review all service areas, see published work, or contact me about a role or project.
Let’s talk
I like working through complex problems with people who care about the details. Have a product to build, an engineering role, or an interesting challenge? Let’s start a conversation.
SaaimOpen to full-time roles, contract work, and conversations about things worth building.