Decoding the Tricky Trio in Multimodal AI: Bias, Hallucination, & Context Switching

The article discusses the challenges in Multimodal AI, including bias, hallucination, and context switching. It suggests solutions for each, such as diversifying training data and including diverse perspectives to mitigate bias, employing thorough testing and explainability techniques to reduce hallucination, and enhancing understanding and tracking of context through advanced techniques for effective context switching.


Challenges in Multimodal AI: Bias, Hallucination, and Context Switching

Challenge Description Potential Solutions

Bias

In the context of Multimodal AI, bias refers to the unfair and often unintended favoritism shown by AI systems. This bias can result from various factors, such as the data used to train the AI system, the algorithm used, and the goals set for the system. AI bias can lead to unjust or prejudiced outcomes, affecting the fairness and accuracy of AI predictions and responses.

Addressing bias in AI involves several strategies. These include diversifying the data used to train AI systems, employing algorithms that can detect and mitigate bias, and continuously monitoring and adjusting AI systems for bias. It's also crucial to include a diverse group of people in the design and testing of AI systems to ensure that a variety of perspectives are considered.

Hallucination

Hallucination in Multimodal AI refers to AI systems generating or predicting information that is not present in the input data. This can result in the AI system making assumptions or filling in gaps based on its training, which can lead to incorrect or misleading outputs. Hallucination is particularly challenging in Multimodal AI as it involves multiple types of data, increasing the complexity of the input and the potential for hallucination.

Reducing hallucination in AI requires careful design of the AI system and thorough testing. This includes using high-quality, diverse data for training and ensuring the AI system is not overfitted to the training data. Regularly monitoring and updating the AI system can also help to identify and address hallucination issues. Further, the use of explainability techniques can help understand why the AI system is hallucinating and how to correct it.

Context Switching

Context switching in Multimodal AI is the ability of an AI system to switch between different contexts or topics within a conversation or interaction. This can be challenging for AI systems, as it requires understanding the context, maintaining the context over the course of the interaction, and seamlessly transitioning between different contexts. Failure to effectively manage context switching can lead to disjointed and confusing interactions.

Improving context switching in AI involves enhancing the AI system's understanding and tracking of context, which can be achieved through advanced natural language processing techniques and learning algorithms. Additionally, designing the AI system to consider the user's perspective and needs can help to ensure more natural and effective context switching.



10-challenges-in-multimodal-a    11-how-to-fine-tune-a-multimo    12-security-and-privacy-impli    2-how-multimodal-llms-work-te    3-top-multimodal-models-in-20    4-multimodal-vs-unimodal-llms    5-use-cases-of-multimodal-llm    6-building-apps-with-multimod    7-prompt-engineering-for-mult    8-multimodal-search-and-retri   

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