LLM Training & Model Quality
Train, Evaluate, and Align LLMs That Perform in the Real World
Coaldev helps companies fine-tune, test, and align large language models for safety, reasoning, and reliability. We build data pipelines, evaluation harnesses, and bias-mitigation workflows so your models are production-ready — not just lab-ready.
We trained and evaluated an LLM to better understand real developer intent.
Human-in-the-loop reviews improved task accuracy and user satisfaction.
What We DeliverLLM Quality,
LLM Quality,
From Data to Deployment
Building a high-performing model requires more than good prompts.
Coaldev combines dataset engineering, evaluation frameworks, and alignment tools to make sure your model delivers trustworthy outputs at scale.
LLM Training & Fine-Tuning
We prepare and label domain-specific datasets, run supervised fine-tuning (SFT) and RLHF, and design reproducible pipelines for reasoning, code generation, or instruction following.
Model Evaluation & Safety
Our hybrid evaluation harness blends human ratings (Elo, rubrics) and automated checks for factuality, coherence, tone, and safety. Built for large-scale model testing and iteration.
Alignment & Bias Mitigation
Keep your AI compliant and fair. We perform alignment audits, bias detection, and RLHF guardrail checks using fairness-annotated datasets.
Factuality & Hallucination Control
Human fact-checking, citation validation, and targeted dataset fixes reduce false or misleading outputs.
Custom Benchmarking & Control
We create evaluation sets specific to your domain — from reasoning to adversarial control — proving your model’s readiness before deployment.
We train, tune, and test models built to perform in the real world
How It Works
A Proven Workflow for Safe and Reliable Models
Our LLM training and evaluation process builds confidence in every release.
Define Success Metrics
Establish measurable KPIs (accuracy, coherence, factuality, fairness).
Build Gold-Standard Data
Curate or synthesize training and evaluation datasets.
Fine-Tune & Test
Apply SFT, RLHF, and iterative evaluations for consistent improvements.
Deploy with Monitoring
Continuous drift tracking and safety validation after launch.
We integrate reusable accelerators like RAG-in-a-Box (retrieval pipelines) and custom evaluation harnesses to shorten experimentation cycles without compromising quality.
Why Coaldev
Outcome-First, Capability-Backed
Experts rate and refine outputs for clarity, factuality, and tone.
End-to-end tracking of datasets, parameters, and results.
Continuous monitoring for bias, toxicity, and hallucinations.
ETL, ElasticSearch, PostgreSQL, cloud (AWS, Azure, Linode).
Coaldev’s model quality systems have powered LLMs used in coding assistants, customer-support bots, and education platforms across multiple industries.
Let’s Train Models That You Can Trust
From instruction-tuned models to evaluation pipelines, we help you deliver AI that meets your internal standards — and your users’ expectations.
Case Studies
Evaluation in Action
