LLM Fine-tuning Solutions
Transform general-purpose LLMs, AI Agents, Agentic AI into domain-specific AI systems with tailored data, fine-tuning, alignment and evaluation pipelines.


































LLM Fine-tuning and Alignment Services

Supervised Fine-tuning (SFT)
LQA builds high-quality supervised datasets to adapt pre-trained LLMs into accurate, task-oriented systems through prompt design, response generation, multi-turn dialogue creation, and domain-specific refinement.

Instruction & Reasoning Data Solutions
LQA delivers structured instruction datasets to enhance model reasoning, consistency, and task understanding, including instruction-response pairs, chain-of-thought annotation, and complex scenario design.

Human Preference Ranking (RLHF/DPO)
Our experts evaluate and rank model outputs using RLHF and DPO to align responses with human expectations across single- and multi-turn interactions, ensuring accuracy, clarity, and tone control.

Safety, Bias & Alignment Optimization
LQA enhances LLM accountability by utilizing targeted datasets and testing frameworks that minimize hallucinations, detect harmful content, and ensure adherence to safety and regulatory standards.

LLM Evaluation & Testing
LQA provides comprehensive datasets for LLM evaluation and testing pipelines, enabling the measurement of performance, detection of failures, and validation of model readiness.

Agentic & Tool-use Training Data
LQA develops datasets for agentic AI systems capable of tool use, planning, and multi-step execution, including function calling, workflow orchestration, retrieval-augmented generation (RAG), and task-oriented interaction data.
LLM Fine-tuning Workflow
Requirement Analysis
LQA works closely with clients to define business goals, data sources, and LLM fine-tuning requirements, covering model scope, domain needs, training methods, evaluation criteria, and cost considerations.
Team Setup
A dedicated team of experts are assembled and aligned through onboarding sessions, ensuring consistency in data preparation, annotation standards, and execution from day one.
Pilot & Validation
LQA runs pilot tasks to validate workflows, refine guidelines, and address edge cases early, incorporating feedback to ensure alignment with expected model performance.
Full-scale Execution
We scale LLM training and fine-tuning pipelines with continuous quality control, structured evaluation, and iterative feedback loops to maintain accuracy and consistency.
Improvement
We monitor performance, identify gaps, and optimize datasets and workflows over time, ensuring your LLM systems improve in reliability, safety, and real-world performance.
教育・エドテック
理解度に応じたAI家庭教師の構築、テスト問題や教材の自動生成、記述式回答のリアルタイムな添削とフィードバックを、LLMを用いてサポートします。これにより、生徒一人ひとりに最適化された学習体験を提供し、教育者の業務負担を軽減します。
LQAでのLLM のトレーニング方法
お客様が設定された目標をより短期間で達成することに貢献します。
LLM Fine-tuning Workflow
LQA works closely with clients to define business goals, data sources, and LLM fine-tuning requirements, covering model scope, domain needs, training methods, evaluation criteria, and cost considerations.
A dedicated team of experts are assembled and aligned through onboarding sessions, ensuring consistency in data preparation, annotation standards, and execution from day one.
LQA runs pilot tasks to validate workflows, refine guidelines, and address edge cases early, incorporating feedback to ensure alignment with expected model performance.
We scale LLM training and fine-tuning pipelines with continuous quality control, structured evaluation, and iterative feedback loops to maintain accuracy and consistency.
We monitor performance, identify gaps, and optimize datasets and workflows over time, ensuring your LLM systems improve in reliability, safety, and real-world performance.
Tools and Technologies We Use
























Our 500+ AI Trainers Pool
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Our Experts
Why Choose LQA's LLM Fine-tuning Solutions?

High-quality Outcomes
LQA delivers high-quality datasets for LLM fine-tuning, powered by structured evaluation, rigorous validation, and human-in-the-loop workflows to ensure accuracy and consistency.

Domain-specific Expertise
Our AI trainers combine deep industry knowledge with data expertise to build LLMs that understand specialized terminology, complex workflows, and real-world business contexts.

Global & Multilingual Capability
With a diverse, multilingual workforce, we enable LLM training and evaluation across languages, regions, and cultural contexts for scalable global deployment.

Cost-efficient Scaling
Optimize LLM fine-tuning costs with flexible engagement models and efficient human-in-the-loop workflows, without compromising data quality or model performance.
成功導入事例
昆虫・幼虫の2Dバウンディングボックス・アノテーション
イタリアの大学による政府出資の昆虫・幼虫・感染症媒介研究プロジェクトを支援。昆虫個体群の早期発見と分析精度の向上により、感染症拡大防止に向けた研究の加速に貢献しました。
詳細を見る
農業画像セグメンテーションの自動化支援
デジタルツインおよびLiDARソリューションを展開する韓国企業向け。未加工の膨大な農業画像データに対し、極めて短期間で高品質なセグメンテーション・アノテーションを提供し、プロジェクトの迅速な立ち上げを実現しました。
詳細を見る
小売店舗における商品(SKU)の2Dバウンディングボックス
小売・スーパーマーケット環境における商品(SKU)検知AIの学習データ構築プロジェクト。棚にある商品の正確な認識・自動識別を可能にし、在庫管理システムの精度向上を支援しました。
詳細を見る
自動運転向けポリゴン・アノテーションによる物体分類
自動運転(AV)技術を牽引する韓国の知覚ソフトウェア企業向け。世界中から収集された膨大な走行データに対し、高精度な2Dポリゴンアノテーションを実施し、安全性に直結する物体識別精度の向上を支えています。
4Dデジタルツインプラットフォーム向け建築図面ラベリング
建設業界のDXを推進する4Dデジタルツインプラットフォーム向け。複雑な建築図面や技術データのラベリングを行い、設計データと現場の進捗をリアルタイムに同期させる高度な可視化を支援しました。
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AIトレーニング向けアプリ操作データの収集・記録
人間とAIの相互作用を研究する米国の研究所向け。AIがより人間らしく直感的にデジタルプラットフォームを操作できるよう、リアリティのある膨大なユーザー操作ログとインタラクションデータを収集・提供しました。
詳細を見る
ハンズフリー操作AI向けの視線データ収集
視線のみでデバイスを操作する次世代インターフェースを開発するイスラエルの技術企業向け。手動入力不要のコミュニケーション実現に向け、多様な条件下での大規模な視線データの収集・構造化を行いました。
詳細を見る
建設現場の安全監視システム向け2Dバウンディングボックス
建設現場の安全モニタリングを専門とする韓国のAI企業向け。作業員や危険エリア、安全装備の着用状況をリアルタイムで検知するコンピュータビジョン・システムの構築をデータ側面から強力にバックアップしました。
物流現場のフォークリフト・パレット間動作のキーポイント抽出
スマート倉庫や製造現場のオペレーション監視システム開発。フォークリフトとパレットの相互作用を正確に捉える2Dキーポイントアノテーションを提供し、作業の安全性向上とワークフローの最適化を実現しました。
技術スタック






















FAQs about LLM Fine-tuning
LLM fine-tuning is a post-training process that adapts a pre-trained large language model (LLM) to specific tasks, domains, or business use cases. Instead of training from scratch, fine-tuning uses curated LLM data, including instruction-response pairs, domain-specific content, and human feedback, to improve model accuracy, consistency, and relevance. This process is commonly used to build AI assistants, LLM agents, and enterprise AI applications.
Training an LLM typically involves two main stages: pre-training and post-training. Pre-training uses massive datasets to teach the model general language patterns. Post-training LLM processes, such as supervised fine-tuning (SFT), RLHF, and prompt engineering, refine the model for specific tasks. This stage relies heavily on high-quality data preparation, human-in-the-loop validation, and structured evaluation to ensure reliable outputs.
LLM evaluation is the process of measuring model performance across multiple dimensions, including quality, coherence, safety, and domain relevance. It combines automated benchmarks with human validation to identify weaknesses and ensure the model performs well in real-world scenarios. Strong model evaluation is critical for reducing risks before deployment.
RAG is a method that enhances LLM outputs by retrieving relevant information from external knowledge sources during inference. Instead of relying only on pre-trained knowledge, the model accesses updated data, improving factual accuracy and reducing hallucinations. RAG is widely used in enterprise AI assistants and knowledge-based systems.
The timeline for LLM fine-tuning depends on the scope of the project, including data preparation, training, evaluation, and testing phases. Pilot projects may take a few weeks, while large-scale deployments involving multilingual data, RLHF, and extensive validation can take several months.

