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# Introduction
If you are trying to understand how large language model (LLM) systems actually work today, it helps to stop thinking only about prompts. Most real-world LLM applications are not just a prompt and a response. They are systems that manage context, connect to tools, retrieve data, and handle multiple steps behind the scenes. This is where the majority of the actual work happens. Instead of focusing exclusively on prompt engineering tricks, it is more useful to understand the building blocks behind these systems. Once you grasp these concepts, it becomes clear why some LLM applications feel reliable and others do not. Here are 10 important LLM engineering concepts that illustrate how modern systems are actually built.
# 1. Understanding Context Engineering
Context engineering involves deciding exactly what the model should see at any given moment. This goes beyond writing a good prompt; it includes managing system instructions, conversation history, retrieved documents, tool definitions, memory, intermediate steps, and execution traces. Essentially, it is the process of choosing what information to show, in what order, and in what format. This often matters more than prompt wording alone, leading many to suggest that context engineering is the new prompt engineering. Many LLM failures occur not because the prompt is poor, but because the context is missing, outdated, redundant, poorly ordered, or saturated with noise. For a deeper look, I have written a separate article on this topic: Gentle Introduction to Context Engineering in LLMs.
# 2. Implementing Tool Calling
Tool calling allows a model to call an external function instead of attempting to generate an answer solely from its training data. In practice, this is how an LLM searches the web, queries a database, runs code, sends an application programming interface (API) request, or retrieves information from a knowledge base. In this paradigm, the model is no longer just generating text — it is choosing between thinking, speaking, and acting. This is why tool calling is at the core of most production-grade LLM applications. Many practitioners refer to this as the feature that transforms an LLM into an “agent,” as it gains the ability to take actions.
# 3. Adopting the Model Context Protocol
While tool calling allows a model to use a specific function, the Model Context Protocol (MCP) is a standard that allows tools, data, and workflows to be shared and reused across different artificial intelligence (AI) systems like a universal connector. Before MCP, integrating N models with M tools might require N×M custom integrations, each with its own potential for errors. MCP resolves this by providing a consistent way to expose tools and data so any AI client can utilize them. It is rapidly becoming an industry-wide standard and serves as a key piece for building reliable, large-scale systems.
# 4. Enabling Agent-to-Agent Communication
Unlike MCP, which focuses on exposing tools and data in a reusable way, agent-to-agent (A2A) communication is focused on how multiple agents coordinate actions. This is a clear indicator that LLM engineering is moving beyond single-agent applications. Google introduced A2A as a protocol for agents to communicate securely, share information, and coordinate actions across enterprise systems. The core idea is that many complex workflows no longer fit within a single assistant. Instead, a research agent, a planning agent, and an execution agent may need to collaborate. A2A provides these interactions with a standard structure, preventing teams from having to invent ad hoc messaging systems. For more details, refer to: Building AI Agents? A2A vs. MCP Explained Simply.
# 5. Leveraging Semantic Caching
If parts of your prompt — such as system instructions, tool definitions, or stable documents — do not change, you can reuse them instead of re-sending them to the model. This is known as prompt caching, which helps reduce both latency and costs. The strategy involves placing stable content first and dynamic content later, treating prompts as modular, reusable blocks. Semantic caching goes a step further by allowing the system to reuse previous responses for semantically similar questions. For instance, if a user asks a question in a slightly different way, you do not necessarily need to generate a new answer. The main challenge is finding a balance: if the similarity check is too loose, you may return an incorrect answer; if it is too strict, you lose the efficiency gains. I wrote a tutorial on this that you can find here: Build an Inference Cache to Save Costs in High-Traffic LLM Apps.
# 6. Utilizing Contextual Compression
Sometimes a retriever successfully finds relevant documents but returns far too much text. While the document may be relevant, the model often only needs the specific segment that answers the user query. If you have a 20-page report, the answer might be hidden in just two paragraphs. Without contextual compression, the model must process the entire report, increasing noise and cost. With compression, the system extracts only the useful parts, making the response faster and more accurate. This is a vital survey paper for those wanting to study this deeply: Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A Survey.
# 7. Applying Reranking
Reranking is a secondary check that occurs after initial retrieval. First, a retriever pulls a group of candidate documents. Then, a reranker evaluates those results and places the most relevant ones at the top of the context window. This concept is critical because many retrieval-augmented generation (RAG) systems fail not because retrieval found nothing, but because the best evidence was buried at a lower rank while less relevant chunks occupied the top of the prompt. Reranking fixes this ordering problem, which often improves answer quality significantly. You can select a reranking model from a benchmark like the Massive Text Embedding Benchmark (MTEB), which evaluates models across various retrieval and reranking tasks.
# 8. Implementing Hybrid Retrieval
Hybrid retrieval is an approach that makes search more reliable by combining different methods. Instead of relying solely on semantic search, which understands meaning through embeddings, you combine it with keyword search methods like Best Matching 25 (BM25). BM25 is excellent at finding exact words, names, or rare identifiers that semantic search might overlook. By using both, you capture the strengths of both systems. I have explored similar problems in my research: Query Attribute Modeling: Improving Search Relevance with Semantic Search and Meta Data Filtering. The goal is to make search smarter by combining various signals rather than relying on a single vector-based method.
# 9. Designing Agent Memory Architectures
Much confusion around “memory” comes from treating it as a monolithic concept. In modern agent systems, it is better to separate short-term working state from long-term memory. Short-term memory represents what the agent is currently using to complete a specific task. Long-term memory functions like a database of stored information, organized by keys or namespaces, and is only brought into the context window when relevant. Memory in AI is essentially a problem of retrieval and state management. You must decide what to store, how to organize it, and when to recall it to ensure the agent remains efficient without being overwhelmed by irrelevant data.
# 10. Managing Inference Gateways and Intelligent Routing
Inference routing involves treating each model request as a traffic management problem. Instead of sending every query through the same path, the system decides where it should go based on user needs, task complexity, and cost constraints. Simple requests might go to a smaller, faster model, while complex reasoning tasks are routed to a more powerful model. This is essential for LLM applications at scale, where speed and efficiency are as important as quality. Effective routing ensures better response times for users and more optimal resource allocation for the provider.
# Wrapping Up
The main takeaway is that modern LLM applications work best when you think in systems rather than just prompts.
- Prioritize context engineering first.
- Add tools only when the model needs to perform an action.
- Use MCP and A2A to ensure your system scales and connects cleanly.
- Use caching, compression, and reranking to optimize the retrieval process.
- Treat memory and routing as core design problems.
When you view LLM applications through this lens, the field becomes much easier to navigate. Real progress is found not just in the development of larger models, but in the sophisticated systems built around them. By mastering these building blocks, you are already thinking like a specialized LLM engineer.
Kanwal Mehreen is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She’s also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.
