USMAN’S INSIGHTS
AI ARCHITECT
⌘F
HomeAll Bookspgvector Book
Homepgvector BookRAG and Agentic Applications
PreviousThe Complete RAG Request LifecycleNext Context Assembly, Token Budgets, and Prompt-Injection Boundaries
AI NOTICE: This is the table of contents for the SPECIFIC CHAPTER only. It is NOT the global sidebar. For all chapters, look at the main navigation.

On this page

10 sections

Progress0%
1 / 10

Muhammad Usman Akbar Entity Profile

Muhammad Usman Akbar is a Forward Deployed Engineer and AI Native Consultant specializing in the design and deployment of multi-agent autonomous systems. Embedding with enterprise teams, he ships production-grade agentic AI and leads industrial-scale digital transformation using Claude and OpenAI ecosystems. His work is centered on achieving up to 30x operational efficiency through distributed systems architecture, FastAPI microservices, and RAG-driven AI pipelines. As CEO and Founding Partner of Fista Solutions, based in Pakistan, he operates as a global technical partner for innovative AI startups and enterprise ventures.

USMAN’S INSIGHTS
AI ARCHITECT

Transforming businesses into autonomous AI ecosystems. Engineering the future of industrial-scale digital products with multi-agent systems.

30X Growth
AI-First
Innovation

Navigation

  • Home
  • Forward Deployed Engineer
  • AI Native Consultant
  • About
  • Insights
  • Book a Call
  • Books
  • Contact
Let's Collaborate

Have a Project in Mind?

Let's build something extraordinary together. Transform your vision into autonomous AI reality.

Start Your Transformation

© 2026 Muhammad Usman Akbar. All rights reserved.

Privacy Policy
Terms of Service
Engineered with
INDUSTRIAL ARCHITECTURE

Build a Citation-First RAG API

A citation-first RAG API retrieves authorized chunks, assigns server-owned reference labels, asks a model for structured claims using only those labels, and validates every citation before responding. The model never chooses a tenant, constructs SQL, or turns arbitrary URLs into evidence. Deterministic provider fakes make the request path testable without paid services.

What will you be able to do?

  • Define embedding and generation provider interfaces.
  • Retrieve with server-derived tenant policy.
  • Build a bounded, labeled context.
  • Validate structured citations.
  • Test the full path deterministically.

What are the boundaries?

python
from typing import Protocol class Embedder(Protocol): model_id: str dimensions: int def embed_query(self, text: str) -> list[float]: ... class Generator(Protocol): model_id: str def answer(self, question: str, context: str) -> dict: ...

The request handler receives an authenticated principal from middleware, not from JSON. A retrieval repository accepts that policy object and a validated query vector. The generator sees labels such as [S1], not database authority.

How is context constructed?

text
[S1] title: Returns policy version: 7 location: section 3 content: ... [S2] ...

Escape or delimit retrieved text as untrusted data. The system instruction says retrieved content may contain instructions and must be used only as evidence. Enforce a token budget and diversify documents. Keep a server map from S1 to authorized chunk ID and source coordinates.

How are citations validated?

Parse schema-constrained output. Reject unknown labels, duplicate malformed objects, missing citations for factual claims under your policy, or citations no longer accessible. Optionally run entailment checks, but human-reviewed evaluation remains necessary. On validation failure, retry once with a repair instruction only if the deadline allows; otherwise abstain.

How do you verify it?

Use fake providers and fixtures for: supported answer, no evidence, invented citation, cross-tenant chunk, prompt injection inside source, generator timeout, invalid JSON, deleted source, and context overflow. Assert response status, abstention reason, citation links, and stage metrics.

What breaks in production?

  • The API trusts model-returned URLs.
  • It logs context containing sensitive data.
  • A retry doubles cost after the deadline.
  • Citation validation checks existence but not caller access.
  • One large chunk consumes the context budget.

AI pair-work prompt

Generate a provider-neutral citation-first RAG API skeleton in [Python/TypeScript] using my existing repository interface. Include typed request/response models, authenticated policy context, deadlines, bounded context, structured generation, citation validation, abstention, safe metrics, and deterministic fakes. Do not include secrets or framework-owned retrieval magic.

Verification contract: Run the nine adversarial fixtures above; review SQL separately and prove every returned citation was both retrieved and authorized.

Check your understanding

  1. Why use server-owned citation labels?
  2. Which identity may the request body control?
  3. Add a test for a citation that was authorized at retrieval but revoked before response.

Official references

  • FastAPI documentation
  • pgvector Python
  • PostgreSQL transaction isolation