Prompts Engineering with RAG & Vector Query Optimization!

Retrieval-Augmented Generation (RAG) leverages external document retrieval to enhance the accuracy of language model outputs, making it ideal for dynamic, fact-based tasks. Effective prompt engineering and optimizing queries for vector databases are crucial for improving the relevance and precision of retrieval-based systems.


Topic Description
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a groundbreaking approach that combines the power of information retrieval systems with the natural language generation capabilities of large language models. In RAG architectures, instead of relying solely on the model's pre-trained knowledge, external documents or facts are retrieved via a specialized retrieval system (such as a vector database). These retrieved materials are used to enhance the quality and accuracy of the generated output. It’s particularly useful for generating up-to-date, factually correct answers, as well as being applicable in domains such as customer support, academic assistance, and enterprise data handling.
Prompt Engineering: Structuring Prompts for Better Document Retrieval
Prompt engineering is the art of designing effective input prompts to optimize the performance of language models. When combined with document retrieval-based systems, structuring a good prompt ensures that the retrieval system and the language model collaborate seamlessly to deliver accurate results. Effective prompts should:
  • Provide clear instructions: Specify the task, context, or expected output format explicitly.
  • Minimize ambiguity: Use specific language to ensure the model stays focused on the task.
  • Define constraints: For example, specifying keywords or formats for answers may deliver more focused outputs.
Experimenting with phrasing, context inclusion, and adjustable parameters is vital to achieving optimal results when working with retrieval-augmented systems.
Optimizing Queries for Vector Databases
Vector databases are core tools for efficiently retrieving relevant documents or pieces of information stored in vectorized format. Optimizing queries for vector databases involves crafting inputs that maximize precision and relevance. Techniques for optimization include:
  • Embedding Fine-Tuning: Ensure embeddings used for queries match the context and domain of the database through fine-tuning processes.
  • Query Contextualization: Provide a rich, context-aware input to the database so that retrieval prioritizes the most relevant matches.
  • Normalization: Maintain consistency between vector representations of both queries and stored data (e.g., through dimension alignment or preprocessing).
  • Experiment with Thresholds: Adjust cosine similarity thresholds to balance inclusion and exclusion of retrieved items.
The role of optimization grows even more critical as datasets scale, ensuring that users can interact with vast information efficiently and accurately.



Adapative-prompting    Error-handling-and-debugging    Ethical-consideration-in-prom    Integrate-prompt-engineer-wit    Llm-fine-tuning-vs-prompt-eng    Multi-turn-prompting    Prompt-engineering-for-agent-    Prompt-engineering-for-multi-    Prompt-engineering-techniques    Prompt-engineering-with-rag   

Dataknobs Blog

Showcase: 10 Production Use Cases

10 Use Cases Built By Dataknobs

Dataknobs delivers real, shipped outcomes across finance, healthcare, real estate, e‑commerce, and more—powered by GenAI, Agentic workflows, and classic ML. Explore detailed walk‑throughs of projects like Earnings Call Insights, E‑commerce Analytics with GenAI, Financial Planner AI, Kreatebots, Kreate Websites, Kreate CMS, Travel Agent Website, and Real Estate Agent tools.

Data Product Approach

Why Build Data Products

Companies should build data products because they transform raw data into actionable, reusable assets that directly drive business outcomes. Instead of treating data as a byproduct of operations, a data product approach emphasizes usability, governance, and value creation. Ultimately, they turn data from a cost center into a growth engine, unlocking compounding value across every function of the enterprise.

AI Agent for Business Analysis

Analyze reports, dashboard and determine To-do

Our structured‑data analysis agent connects to CSVs, SQL, and APIs; auto‑detects schemas; and standardizes formats. It finds trends, anomalies, correlations, and revenue opportunities using statistics, heuristics, and LLM reasoning. The output is crisp: prioritized insights and an action‑ready To‑Do list for operators and analysts.

AI Agent Tutorial

Agent AI Tutorial

Dive into slides and a hands‑on guide to agentic systems—perception, planning, memory, and action. Learn how agents coordinate tools, adapt via feedback, and make decisions in dynamic environments for automation, assistants, and robotics.

Build Data Products

How Dataknobs help in building data products

GenAI and Agentic AI accelerate data‑product development: generate synthetic data, enrich datasets, summarize and reason over large corpora, and automate reporting. Use them to detect anomalies, surface drivers, and power predictive models—while keeping humans in the loop for control and safety.

KreateHub

Create New knowledge with Prompt library

KreateHub turns prompts into reusable knowledge assets—experiment, track variants, and compose chains that transform raw data into decisions. It’s your workspace for rapid iteration, governance, and measurable impact.

Build Budget Plan for GenAI

CIO Guide to create GenAI Budget for 2025

A pragmatic playbook for CIOs/CTOs: scope the stack, forecast usage, model costs, and sequence investments across infra, safety, and business use cases. Apply the framework to IT first, then scale to enterprise functions.

RAG for Unstructured & Structured Data

RAG Use Cases and Implementation

Explore practical RAG patterns: unstructured corpora, tabular/SQL retrieval, and guardrails for accuracy and compliance. Implementation notes included.

Why knobs matter

Knobs are levers using which you manage output

The Drivetrain approach frames product building in four steps; “knobs” are the controllable inputs that move outcomes. Design clear metrics, expose the right levers, and iterate—control leads to compounding impact.

Our Products

KreateBots

Launch intelligent chatbots instantly
  • Ready-to-use front-end—configure in minutes
  • Admin dashboard for full chatbot control
  • Integrated prompt management system
  • Personalization and memory modules
  • Conversation tracking and analytics
  • Continuous feedback learning loop
  • Deploy across GCP, Azure, or AWS
  • Add Retrieval-Augmented Generation (RAG) in seconds
  • Auto-generate FAQs for user queries

    • KreateWebsites

    AI-driven website builder
  • Build SEO-optimized sites powered by LLMs
  • Host on Azure, GCP, or AWS
  • Intelligent AI website designer
  • Agent-assisted website generation
  • End-to-end content automation
  • Content management for AI-driven websites
  • Available as SaaS or managed solution
  • Listed on Azure Marketplace

    • Kreate CMS

    Content Management for GenAI
  • Purpose-built CMS for AI content pipelines
  • Track provenance for AI vs human edits
  • Monitor lineage and version history
  • Identify all pages using specific content
  • Remove or update AI-generated assets safely

    • Generate Slides

    Create presentations from prompts
  • Instant slide decks from natural language prompts
  • Convert slides into interactive webpages
  • Optimize presentation pages for SEO

    • Content Compass

    Automated storytelling engine
  • Auto-generate articles and blogs
  • Create and embed matching visuals
  • Link related topics for SEO ranking
  • AI-driven topic and content recommendations