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Pamela
Prompt Training

RAG for LLMs : Models Tailored to Industry Needs

LLM Retrieval-Augmented Generation (RAG) combines the power of language models with external data sources to create multimodal and verticalized applications. These advanced models are specifically tailored to meet the demands of various industries, offering precision and adaptability in AI-driven solutions.

Transforming AI with RAG-Enhanced LLMs
Unlocking the Power of Multimodal Integration and Industry-Specific Applications

1.
Understanding LLM RAG

LLM RAG enhances traditional language models by integrating them with external knowledge bases, databases, and multimodal inputs like text, images, or audio. This integration allows for more accurate, context-aware outputs, making these models particularly valuable in specialized domains.

2.
Advantages of Multimodal and Verticalized LLMs

Multimodal and verticalized LLMs deliver context-rich responses tailored to specific industries. By incorporating external data, these models enhance accuracy, provide flexibility in handling diverse data types, and improve relevance and user experience across specialized applications.

3.
Industries Benefiting from LLM RAG

LLM RAG models are well-suited for various industries. In healthcare, they aid in interpreting medical data; in finance, they enhance risk analysis and decision-making; in retail, they improve customer interactions by integrating product data and user preferences.

Data Labeling Workflows built with Ethics.
Everything you need to get your data perfectly labeled, from prompt engineering to model evaluation.

TOP Data LABELING 
& GenAI tools
We label on our tool, yours or one of our partner’s tool.
TAILORED DATA LABELING WORKFLOWS
We ensure successful data labeling with tailored workflows.
AGILE PROJECT MANAGEMENT TEAM
We ensure efficiency with dedicated Data Experts.
SOLID HUMAN-IN-THE-LOOP
Working with a diverse, qualified and trained labelers.

INTEGRATED

API integration to your own systems.

PAY-AS-YOU-GO

Competitive market pricing solutions.

Customized

Tailored workforce from all over the world.

EthicAL

Social impact controlled and measured.
Talk to us

Diverse Applications of Fine-Tuned LLMs

Explore how fine-tuned LLMs can revolutionize specialized tasks across various industries, delivering precision and tailored performance.

DATA 
COLLECTION AND MODEL CREATION

  • Gather, create, or curate prompt-generation for your Generative AI model

  • Ask Data experts to enhance model accuracy

RLHF

  • Evaluate and rank prompts and results

  • Get human feedback to score and categorize responses

  • Conduct model evaluations to refine performance

CONTENT MODERATION

  • Identify and remove negative generated content

  • Review prompts and outputs for potential issues, with adversarial testing

REAL-TIME SUPPORT

  • Provide real-time support for Generative AI models in production

  • Conduct ongoing human verification and confirmation for classifier support

Why choosing isahit?
Leverage skilled on-demand workforce for ethical AI projects using top AI tools.

THE Only
ETHICAL
CHOICE

We place impact at the heart of our business model and measure it every year, making us the first B Corp certified AI company in Europe.

THE MOST DIVERSIFIED WORKFORCE

Our workforce is multicultural, coming from 44 different countries, speaking more than 16 languages, with different academic backgrounds and professional experiences.

A HIGHLY
TRAINED WORKFORCE

We assign our workforce to projects based on their skills and then provide them with a complete onboarding (more than 3 hours of training per project) & ongoing coaching.

THE MOST
AGILITY SOLUTIONS

We understand our customer's needs for flexibility and offer them appropriate solutions: scalable workforce, tools for every labelling needs, pay as you go system.

Our Customers
We helped them getting clean datasets

USE CASE : L'Oréal

Discover how L'Oréal uses our image annotation service to train their facial recognition algorithm and capitalise on the diversity of our workforce to avoid including biases in their models.
  • Use of a consensus process

  • Assigning images according to the skin type : (Indian, Asian, African, American, Caucasian)

  • Order of points

USE CASE : Airbus

Find out how Airbus uses our image annotation service to train its recognition algorithms on satellite imagery and capitalises on the flexibility of our service for mass annotations on an ad hoc basis.
  • Process of managing fluctuating image flows

  • Optimisation of the tool to handle several hundred annotations per image

  • Use of the directional bounding box with directional vector

USE CASE : Sodexo

Come and see how Sodexo uses our image annotation service to train their Food Recognition algorithm and capitalise on the diversity of our workforce to avoid bias in their models.
  • A tailor-made annotation pipeline

  • A tailor-made API

  • Specific interface for label management