LLM Application Development
Learn to design, build, and deploy production‑ready LLM‑driven applications, covering API integration, prompt engineering, retrieval‑augmented generation, agent tool use, and fine‑tuning strategies, while mastering architectural patterns, evaluation pipelines, and scalability considerations.
Who Should Take This
Software engineers, data scientists, and product technologists who have experience building APIs or micro‑services and want to extend their skill set to LLM‑centric solutions. They will gain practical knowledge of prompt design, retrieval‑augmented pipelines, agent orchestration, and fine‑tuning, enabling them to prototype, evaluate, and ship robust AI features at scale.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
61 learning goals
1
LLM APIs and Integration
6 topics
Describe LLM API concepts including chat completions, system prompts, temperature, top-p sampling, max tokens, stop sequences, and the request-response lifecycle for API-based inference
Apply LLM API integration including authentication, error handling, rate limiting, retry logic, streaming responses, and cost tracking for production API usage
Apply structured output techniques including JSON mode, function calling, tool use, and schema-constrained generation to produce reliable machine-parseable LLM outputs
Analyze LLM provider selection including model capabilities, pricing models, latency characteristics, context window sizes, and the trade-offs between hosted APIs and self-hosted models
Apply multi-model routing including using different models for different task complexities, cascading from small to large models on failure, and implementing intelligent model selection logic
Apply batch processing for LLM APIs including async request patterns, batch endpoints, queue-based architectures, and cost optimization through off-peak batch processing
2
Prompt Engineering for Applications
6 topics
Describe prompt engineering fundamentals including zero-shot, few-shot, and chain-of-thought prompting and explain how prompt structure affects model reasoning and output quality
Apply system prompt design including role definition, behavioral constraints, output format specification, and safety guardrails for consistent and controllable LLM behavior
Apply advanced prompting techniques including step-by-step reasoning, self-consistency, tree of thoughts, and prompt chaining to decompose complex tasks into manageable subtasks
Apply prompt testing and evaluation including creating test suites, measuring output quality metrics, regression testing prompts across model versions, and A/B testing prompt variants
Analyze prompt optimization trade-offs including prompt length versus cost, specificity versus generalization, and the diminishing returns of increasingly complex prompt engineering
Apply dynamic prompt construction including template systems, conditional sections, context-aware instruction assembly, and managing prompts as software artifacts with version control
3
Retrieval-Augmented Generation
8 topics
Describe retrieval-augmented generation architecture including the indexing pipeline, retrieval step, context injection, and generation step and explain how RAG reduces hallucination
Apply document processing for RAG including chunking strategies, metadata extraction, recursive splitting, semantic chunking, and the impact of chunk size on retrieval quality
Apply embedding models for RAG including text embedding selection, dimensionality considerations, batch embedding pipelines, and embedding model fine-tuning for domain-specific retrieval
Apply vector database operations including indexing, similarity search with HNSW and IVF algorithms, metadata filtering, and hybrid search combining dense and sparse retrieval
Apply RAG pipeline optimization including re-ranking, query transformation, hypothetical document embeddings, multi-query retrieval, and context window management for improved answer quality
Analyze RAG system evaluation including retrieval recall and precision, answer faithfulness, answer relevancy, context utilization metrics, and end-to-end evaluation frameworks
Apply knowledge graph-enhanced RAG including structured knowledge retrieval, entity-aware chunking, and how graph traversal complements vector similarity for multi-hop reasoning
Analyze RAG architecture decisions including when to use naive RAG versus advanced RAG versus modular RAG patterns based on accuracy requirements, latency budget, and data characteristics
4
LLM Agents and Tool Use
7 topics
Describe LLM agent concepts including the observe-think-act loop, tool use, planning, memory, and how agents extend LLMs from passive text generators to autonomous task executors
Apply tool integration for LLM agents including function definitions, tool schemas, API connectors, and sandboxed code execution environments for safe agent operations
Apply agent orchestration patterns including ReAct, plan-and-execute, multi-agent collaboration, supervisor agents, and swarm architectures for complex task decomposition
Apply agent memory systems including conversation history management, summarization, long-term memory stores, and how memory persistence enables agents to maintain context across sessions
Analyze agent reliability including failure modes, hallucinated tool calls, infinite loops, cost runaway, safety guardrails, and human-in-the-loop checkpoints for production agent systems
Apply human-in-the-loop agent design including approval gates, confidence-based escalation, and how to design agent workflows that maintain human oversight without sacrificing automation benefits
Apply code execution agents including sandboxed environments, artifact management, iterative debugging loops, and how code-writing agents verify their own output through test execution
5
Fine-Tuning and Adaptation
7 topics
Describe LLM fine-tuning concepts including when fine-tuning outperforms prompting, training data requirements, and the distinction between full fine-tuning and parameter-efficient methods
Apply parameter-efficient fine-tuning including LoRA, QLoRA, prefix tuning, and adapter layers and explain how they reduce compute and memory requirements while maintaining performance
Apply training data preparation for fine-tuning including instruction formatting, conversation templates, data cleaning, deduplication, and quality filtering for supervised fine-tuning
Apply fine-tuning evaluation including held-out test sets, task-specific benchmarks, human evaluation, and detecting catastrophic forgetting of general capabilities after fine-tuning
Analyze the decision framework for prompting versus RAG versus fine-tuning including cost, latency, accuracy, maintenance burden, and data privacy considerations for each approach
Apply distillation from large to small models including using large model outputs as training data, knowledge distillation for specialized tasks, and cost reduction through model compression
Describe synthetic data generation using LLMs including generating training examples, data augmentation through paraphrasing, and quality filtering pipelines for LLM-generated training data
6
LLM Evaluation and Testing
6 topics
Describe LLM evaluation challenges including the subjectivity of open-ended generation, benchmark contamination, and why traditional ML metrics are insufficient for language generation
Apply automated evaluation methods including LLM-as-judge, reference-based metrics, embedding similarity, and rubric-based scoring for scalable quality assessment
Apply evaluation frameworks including RAGAS, DeepEval, and custom evaluation harnesses to build systematic testing pipelines for LLM applications across multiple quality dimensions
Analyze evaluation strategy design including metric selection for different task types, statistical significance of comparisons, and building evaluation datasets that reflect production distribution
Apply benchmark-driven evaluation including selecting appropriate benchmarks for different capabilities, designing custom evaluation datasets, and avoiding benchmark contamination in model selection
Apply red-teaming for LLM applications including adversarial testing methodologies, automated red-team generation, and how systematic adversarial evaluation improves application robustness before launch
7
LLM Safety and Security
5 topics
Describe LLM safety concepts including prompt injection, jailbreaking, data leakage, hallucination, and the attack surface of LLM-powered applications exposed to user input
Apply input validation and output filtering including content moderation, PII detection, prompt injection detection, and output sanitization to protect LLM applications from misuse
Apply guardrail frameworks including Guardrails AI, NeMo Guardrails, and custom rule engines to enforce behavioral constraints and safety policies on LLM outputs
Analyze defense-in-depth strategies for LLM applications including layered security, red-teaming methodologies, adversarial testing, and the arms race between attacks and defenses
Apply rate limiting and abuse prevention including detecting automated misuse, implementing usage quotas, and designing escalation paths for policy violations in LLM-powered applications
8
Production LLM Systems
6 topics
Apply LLM application architecture patterns including gateway routing, fallback chains, caching layers, and how to design systems that gracefully handle API outages and rate limits
Apply cost optimization for LLM applications including prompt compression, response caching, model routing based on complexity, and batch processing for non-real-time workloads
Apply observability for LLM applications including logging prompts and completions, latency tracking, token usage monitoring, and quality regression detection in production
Analyze LLM application scalability including horizontal scaling patterns, queue-based architectures, streaming versus synchronous interfaces, and capacity planning for variable-length LLM calls
Apply semantic caching for LLM responses including embedding-based cache lookup, cache invalidation strategies, and how semantic caching reduces cost for similar but not identical queries
Analyze LLM application testing in CI/CD including prompt regression testing, evaluation pipeline integration, model version pinning, and handling non-deterministic outputs in test assertions
9
Multimodal LLM Applications
4 topics
Describe multimodal LLM capabilities including vision-language models, image understanding, document analysis, and how multimodal models extend text-only LLM applications
Apply multimodal input processing including image encoding, document parsing, audio transcription, and how to design prompts that effectively combine text and visual information
Analyze multimodal application design including cost implications of image tokens, latency trade-offs, and the current capabilities and limitations of vision-language models for production use
Apply audio and speech integration including speech-to-text for input, text-to-speech for output, and building voice-enabled LLM applications with real-time audio streaming
10
LLM Application Frameworks
6 topics
Describe LLM application frameworks including LangChain, LlamaIndex, Haystack, and Semantic Kernel and explain how they provide abstractions for common LLM application patterns
Apply chain and pipeline composition using LLM frameworks to build multi-step workflows including sequential chains, branching logic, and error handling in orchestrated LLM calls
Analyze framework selection trade-offs including abstraction level, vendor lock-in, community support, performance overhead, and when to use frameworks versus direct API integration
Apply observability integration with LLM frameworks including tracing LLM calls, cost attribution per pipeline step, and debugging complex multi-step LLM workflows in production
Describe the Claude API and Anthropic SDK including message-based API design, tool use, streaming, and how to build applications using Anthropic's model family for production use cases
Describe the OpenAI API and SDK including chat completions, function calling, assistants API, and how to build applications using OpenAI's model family with proper error handling
Hands-On Labs
15 labs
~425 min total
Console Simulator
Code Sandbox
Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.
Scope
Included Topics
- LLM API integration (chat completions, structured outputs, function calling), prompt engineering for applications, retrieval-augmented generation (chunking, embeddings, vector databases, re-ranking), LLM agents and tool use, fine-tuning (LoRA, QLoRA), LLM evaluation and testing, safety and security (prompt injection, guardrails), production patterns (caching, routing, observability), multimodal applications, LLM frameworks (LangChain, LlamaIndex)
Not Covered
- LLM pretraining and architecture internals (covered in Deep Learning and NLP domains)
- Specific cloud provider AI services (covered in certification tracks)
- Academic NLP research and benchmark analysis
- Frontend UI/UX design for AI applications
- Business strategy for AI product development
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