Ever needed a quick technical answer but had to dig through Google Drive, Confluence, emails, or Slack—only to end up unsure whether you found the latest version? With RAG AI (Retrieval-Augmented Generation), your team can ask questions in natural language and receive source-cited answers pulled directly from your internal documents.
In this post, NKKTech Global shares a demo experience: type a question → get technical information instantly—built for Engineering, Product, QA, Support, and cross-functional teams that need reliable knowledge on demand.
What is RAG AI—and why does it matter for companies?
RAG AI combines two core components:
- Retriever: finds the most relevant passages from your knowledge base (PDFs, docs, wikis, tickets, specs, SOPs, etc.)
- Generator: produces a clear, structured answer grounded in those retrieved passages—often with citations and links
The key benefit: RAG helps reduce “confident but wrong” responses because the answer is anchored to your company’s real documentation.
As an AI company focused on practical enterprise deployments, NKKTech Global builds RAG systems that prioritize: accuracy, traceability, and real-world usability.
How the demo works (Ask → Retrieve → Answer)
The NKKTech RAG AI demo is designed to be simple for anyone to use:
Step 1: Ask a question like you normally would
Examples:
- “What’s the staging deployment process for Project A?”
- “Which fields are required for the invoice creation API?”
- “How is OT and leave calculated in our HRM rules?”
- “What is the retry policy when Redis times out?”
Step 2: The system retrieves relevant documents and sections
RAG AI searches across your internal knowledge sources such as:
- Technical documentation, PRDs, SRS
- Runbooks, SOPs, operational guides
- READMEs, API specs, database schemas
- Meeting notes / decision logs (if included in the knowledge base)
Step 3: You get a structured answer with sources
Typical output includes:
- A clear summary
- Technical details (steps, parameters, constraints, notes)
- Source references (links to documents and cited passages)
This makes it easy to verify and align on the same “single source of truth.”
What you gain from NKKTech’s RAG AI experience
1) Faster technical lookup, fewer interruptions
Instead of spending 30–60 minutes searching—or pinging senior engineers—you can get answers in seconds.
2) Knowledge consistency across teams
RAG AI reduces dependency on “the one person who knows” and supports:
- faster onboarding
- smoother handovers
- distributed teams across projects and time zones
3) Fewer wrong answers thanks to traceable citations
Citations help with:
- quick validation
- auditability
- reducing debates and miscommunication
4) Works with multiple document types and workflows
Depending on your needs, NKKTech can ingest and search:
- PDFs, Word docs, Excel files, wiki pages
- ticketing systems and operational notes
- SOPs for HR/Finance with proper access control
Common real-world use cases (what teams actually ask)
- Engineering/DevOps: deployment/rollback steps, environment configs, migration routines, release checklists
- Backend/Frontend: API specs, request/response formats, auth flows, coding conventions
- QA: test standards, acceptance criteria, business flows aligned with PRD
- Support/CS: troubleshooting guides, SOP-based responses, frequent issue handling
- Product/BA: business rules, feature scope, version changes and decision history
How NKKTech Global implements RAG AI
As an AI company delivering production-ready systems, NKKTech Global typically focuses on:
- Data readiness & access control: who can ask what, and which documents they can see
- Smart chunking & indexing: semantic-friendly document splitting for better retrieval
- Hybrid search (dense + sparse): improves search quality for both keywords and meaning
- Citations & confidence scoring: show sources and reliability signals
- Guardrails: prevent answers beyond the document scope and protect sensitive data
The goal: as easy as chat, but as reliable as documentation.
Want to experience the demo?
If you’d like a demo tailored to your company’s documentation, NKKTech can support:
- setting up a sample knowledge base
- mapping priority sources
- running a demo using real questions from your teams (Engineering / HR / Support / Product)
All you need is a representative document set—or a description of your data sources—and NKKTech Global will propose the right RAG setup.
Quick FAQ
How is RAG AI different from a typical chatbot?
RAG AI answers using your internal documents and provides citations, reducing hallucinations and improving trust.
Can RAG AI handle technical documentation well?
Yes—technical lookup is one of the strongest use cases: API specs, runbooks, SOPs, PRDs, and decision logs.
Does NKKTech Global deliver custom RAG AI deployments?
Yes. NKKTech Global provides consulting, implementation, and optimization of RAG AI based on your data, permissions, and workflow.
Contact Information:
🌐 Website: https://nkk.com.vn
📧 Email: contact@nkk.com.vn
💼 LinkedIn: https://www.linkedin.com/company/nkktech
