Build accurate knowledge assistants using chunking, embeddings, vector DBs, and hybrid search. Tackle hallucinations in enterprise AI.
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Instant assistance for all your AI transformation queries. Seamless enterprise support throughout implementation and beyond.
AI Solutions for our clients
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“While most AI training providers teach how to use AI, LearningMiracle teaches organizations how to build, optimize, govern, secure, scale, and profit from AI.”
Miracle is India’s leading enterprise AI training provider, committed to transforming organizations through cutting-edge AI engineering programs. We specialize in delivering measurable business outcomes — not just AI literacy.
With a team of experts in LLMs, multi-agent systems, RAG engineering, and AI security, we help companies build proprietary AI capabilities and unlock competitive advantages in the AI era.
RAG Engineering LLM Fine-Tuning AI FinOps AI Security LLMOps AgentOps SLM Strategy Enterprise AI governance Multi-Agent Architecture
RAG Engineering LLM Fine-Tuning AI FinOps AI Security LLMOps AgentOps SLM Strategy Enterprise AI governance Multi-Agent Architecture
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| # | Training Program | Why This Training is Needed | Key Agenda / Topics Covered | Training Summary (Crux) | Eligible Audience | Business Outcome |
|---|---|---|---|---|---|---|
| 1 | AI Budget Management & FinOps | Many companies spend ₹10L–₹1Cr+ on AI pilots without ROI visibility. Token costs can spiral out of control. | AI Cost Models, Token Economics, Cost Tracking, ROI Measurement, Budget Governance | Learn how to control AI spending, predict costs, and maximize ROI. | CTO, CFO, CIO, AI Leads, Engineering Managers | 30-50% reduction in AI spending |
| 2 | Token Optimization Masterclass | LLM costs increase exponentially with scale. Poor prompts waste thousands of dollars. | Prompt Compression, Context Optimization, Caching, Semantic Retrieval | Reduce token consumption while improving output quality. | AI Engineers, Developers, Solution Architects | 20-80% token savings |
| 3 | LLM Cost Reduction Strategies | Companies are overusing expensive models for simple tasks. | Model Routing, Distillation, Quantization, SLM Strategy | Learn how to select the right model for the right task. | AI Teams, Product Teams | Significant AI infrastructure savings |
| 4 | Fine-Tuning LLMs End-to-End | Generic LLMs lack industry-specific knowledge. | LoRA, QLoRA, PEFT, Dataset Preparation, Evaluation | Build domain-specific AI assistants efficiently. | AI Engineers, Data Scientists | Customized enterprise AI models |
| 5 | Low-Cost LLM Training Techniques | Training large models is expensive and inaccessible. | Synthetic Data, Distillation, Transfer Learning, Open Source Models | Train smarter rather than spending millions. | AI Researchers, ML Engineers | Lower model development costs |
| 6 | Advanced RAG Engineering | Hallucinations remain a major challenge in enterprise AI. | Chunking, Embeddings, Vector DBs, Hybrid Search, Metadata Filters | Build accurate knowledge assistants. | AI Engineers, Architects | Improved AI accuracy |
| 7 | RAG Optimization & Performance Engineering | Slow responses and poor retrieval reduce adoption. | Retrieval Tuning, Latency Optimization, Cost Optimization | Make enterprise RAG systems scalable. | AI Teams | Better performance & lower costs |
| 8 | Enterprise Knowledge Assistant Development | Organizations struggle to unlock internal knowledge. | Internal GPTs, SharePoint Integration, Knowledge Graphs | Create ChatGPT-like enterprise assistants. | Digital Transformation Teams | Productivity improvement |
| 9 | Multi-Agent Systems Architecture | Single-agent systems struggle with complex workflows. | Agent Collaboration, Orchestration, Agent Communication | Design scalable multi-agent ecosystems. | AI Architects, CTOs | Enterprise automation at scale |
| 10 | Parallel AI Agents Design | Sequential execution increases cost and response time. | Concurrent Agents, Task Delegation, Workflow Design | Build high-speed AI systems using parallel execution. | AI Engineers | Faster execution, lower costs |
| 11 | Agent Memory Optimization | Long conversations consume excessive tokens. | Memory Layers, Vector Memory, Context Management | Reduce token usage through intelligent memory. | AI Engineers | Lower operational costs |
| 12 | AgentOps Engineering | AI agents fail in production due to lack of monitoring. | Monitoring, Logging, Deployment, Governance | Learn production-grade agent operations. | DevOps, AI Teams | Reliable AI systems |
| 13 | AI Security & Prompt Injection Defense | Prompt attacks and data leakage are growing threats. | Jailbreaks, Prompt Injection, Security Testing | Protect enterprise AI systems. | Security Teams, AI Teams | Reduced AI security risks |
| 14 | Secure Enterprise AI Deployment | Enterprises fear compliance and data exposure. | Access Control, Governance, Data Protection | Deploy AI safely and compliantly. | CIOs, CISOs | Enterprise-ready AI adoption |
| 15 | LLMOps & AI Operations | AI projects fail during deployment and maintenance. | Monitoring, Version Control, CI/CD for AI | Run AI reliably in production. | DevOps, AI Teams | Scalable AI operations |
| 16 | AI Observability & Monitoring | Organizations cannot track AI performance effectively. | Drift Detection, Cost Monitoring, Quality Metrics | Gain visibility into AI behavior. | AI Leads | Better governance |
| 17 | Small Language Models (SLM) for Enterprise | Most business use cases don't require large expensive models. | SLM Selection, Fine-Tuning, Deployment | Build cost-efficient enterprise AI. | CTOs, Architects | Lower AI infrastructure costs |
| 18 | Multimodal AI Engineering | Companies want AI to understand text, images, audio, and video. | Vision Models, OCR, Audio Models, Integration | Build next-generation AI solutions. | AI Engineers | Advanced AI capabilities |
| 19 | AI Product Management | Many AI initiatives lack business alignment. | Product Strategy, Use Cases, ROI Tracking | Manage AI products effectively. | Product Managers, Business Heads | Improved AI adoption |
| 20 | Building SaaS Products with LLMs | AI SaaS startups often fail due to poor architecture. | AI Architecture, Monetization, Scalability | Create scalable AI products. | Founders, Product Teams | Faster go-to-market |
| 21 | AI Strategy for CXOs | Leadership struggles to define AI roadmaps. | AI Governance, Transformation Roadmaps, Investment Planning | Create an enterprise AI strategy. | CEOs, CIOs, CTOs | Better AI investment decisions |
| 22 | AI ROI Measurement Framework | Most AI projects cannot prove business value. | ROI Models, KPI Tracking, Cost-Benefit Analysis | Measure and justify AI investments. | CXOs, Finance Teams | Demonstrable business impact |
| 23 | Enterprise AI Transformation Playbook | Companies don't know where to start AI adoption. | AI Maturity Assessment, COE Setup, Governance | End-to-end transformation blueprint. | Leadership Teams | Faster transformation |
| 24 | AI Workforce Transformation | Employees fear AI replacing jobs. | Reskilling, AI Adoption, Human-AI Collaboration | Build an AI-ready workforce. | HR, L&D, Business Leaders | Improved workforce readiness |
| 25 | Building Domain-Specific LLMs | Enterprises need industry-specific intelligence. | Industry Data Preparation, Fine-Tuning, Evaluation | Create proprietary AI assets. | AI Architects, Data Scientists | Competitive advantage |
