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# Introduction
If you're trying to understand how large-scale language modeling (LLM) systems actually work today, it helps to stop thinking only in terms of information. Most real-world LLM applications are not just information and feedback. Systems manage context, connect to tools, retrieve data, and manage many steps behind the scenes. This is where most of the real work happens. Instead of focusing exclusively on agile developer tactics, it's more useful to understand the building blocks behind these systems. Once you understand these concepts, it becomes clear why some LLM applications feel credible and others do not. Here are 10 key LLM engineering concepts that show how modern systems are built.
# 1. Understanding Content Engineering
Content engineering involves deciding what the model should see at any given time. This goes beyond writing good information; includes controlling system instructions, chat history, returned documents, tool definitions, memory, intermediate steps, and execution tracking. Basically, it is the process of choosing what information to display, in what order, and in what format. This often matters more than just quick words, leading many to suggest that context engineering is rapid engineering. Most LLM failures are not because the information is bad, but because the context is lost, outdated, redundant, poorly ordered, or full of noise. For a deeper look, I wrote a separate article on this topic: Gentle Introduction to Content Engineering for LLMs.
# 2. Using the Hitting Tool
Tool calling allows the model to call an external function instead of trying to generate an answer only with its training data. Essentially, this is how LLM searches the web, queries a database, executes code, sends an application programming interface (API) request, or retrieves information from a database. In this paradigm, the model no longer just produces text – it chooses between thinking, speaking, and acting. This is why tooling is at the core of many manufacturing degree LLM programs. Many practitioners refer to this as the factor that turns the LLM into an “agent,” as it gains the ability to take action.
# 3. Adopting the Model Context Protocol
While a tool call allows a model to execute a specific function, i Model Context Protocol (MCP) is a standard that allows tools, data, and workflows to be shared and reused across diverse artificial intelligence (AI) systems as an international connector. Prior to MCP, assembling N models with M tools might require N×M custom assemblies, each with its own error capability. MCP solves this by providing a consistent way to expose tools and data so that any AI client can use them. It is quickly becoming an industry-wide standard and serves as a critical component in 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 focuses on how multiple agents coordinate actions. This is a clear indication that LLM engineering goes beyond single agent applications. Google introduced A2A as a protocol for agents to securely communicate, share information, and coordinate actions across enterprise systems. The main idea is that many complex workflows no longer fit within a single assistant. Instead, the research agent, the planning agent, and the killing agent may have to work together. A2A provides this interaction in a standard structure, preventing groups from having to establish ad hoc messaging systems. For more information, refer to: Building AI Agents? A2A vs. MCP Simply Explained.
# 5. Using Semantic Caching
If parts of your information – such as system instructions, tool definitions, or stable documentation – do not change, you can reuse them instead of reimporting them in the model. This is known as fast caching, which helps reduce both latency and cost. The strategy involves putting stable content first and dynamic content later, treating information as modular, reusable building blocks. Semantic caching goes a step further by allowing the system to reuse previous answers to semantically similar queries. For example, if a user asks a question in a slightly different way, you don't need to generate a new answer. The biggest challenge is to find a balance: if the matching check is too loose, you can return the wrong answer; if it's too tight, you lose the benefits of efficiency. I wrote a tutorial on this which you can find here: Build Inference Cache to Save Costs in High Traffic LLM Applications.
# 6. Using Content Compression
Sometimes the retriever successfully finds the correct text but returns too much text. Although the document may be relevant, the model usually only needs a specific component that answers the user's question. If you have a 20-page report, the answer may be hidden in just two paragraphs. Without context compression, the model must process the entire message, increasing noise and cost. By compressing, the system extracts only the useful parts, making the response faster and more accurate. This is an important survey paper for those who want to study this in depth: Content Compression in Retrieval-Enhanced Generation of Large Language Models: A Survey.
# 7. Using Restructuring
A reset is a second test that occurs after the first reset. First, the retriever retrieves a group of candidate documents. Then, the restorer evaluates those results and places the most relevant ones at the top of the context window. This concept is important because many retrieval-retrieval generation (RAG) systems fail not because the retrieval did not find anything, but because the best evidence is buried at the bottom while the less important pieces hold the top of the information. Reordering fixes this ordering problem, which often improves the quality of the response significantly. You can choose a repositioning model from a benchmark such as Massive Text Embedding Benchmark (MTEB)which tests the models across various retrieval and reorganization tasks.
# 8. Using Hybrid Recovery
Hybrid retrieval is a method that makes the search more reliable by combining different methods. Instead of relying solely on semantic search, which understands meaning by embedding, combining keyword search methods such as Best Matching 25 (BM25). BM25 is very good at finding specific words, terms, or rare identifiers that a semantic search might miss. By using both, you capture the power of both systems. I tested similar problems in my research: Query Attribute Modeling: Improving Search Relevance with Semantic Search and Metadata Filtering. The goal is to make the search smarter by combining various signals rather than relying on a single vector-based approach.
# 9. Agent Memory Structures
Much of the confusion surrounding “memory” comes from treating it as a monolithic concept. In today's agent systems, it is best to separate short-term working state from long-term memory. Short-term memory represents what the agent is currently using to complete a task. Long-term memory acts as a database of stored information, organized by key or namespaces, and only brought to the content window when appropriate. Memory in AI is essentially a problem of retrieval and state management. You have to decide what to keep, how to organize it, and when to remember it to ensure that the agent always runs smoothly without being overwhelmed by unnecessary data.
# 10. Managing Inbound Gateways and Smart Routing
Fuzzy routing involves treating each request model as a traffic management problem. Instead of sending all queries the same way, the system decides where to go based on user needs, task complexity, and cost constraints. Simple applications may go for a smaller, faster model, while complex computing tasks are directed to a more powerful model. This is important for LLM applications at scale, where speed and efficiency are as important as quality. Efficient routing ensures better response times for users and the most appropriate allocation of provider resources.
# Wrapping up
The key takeaway is that modern LLM applications work best when you think in systems rather than precepts.
- Prioritize context engineering first.
- Add tools only if the model needs to perform an action.
- Use MCP and A2A to ensure your system scales and connects cleanly.
- Use caching, compression, and reorganization to improve the retrieval process.
- Treat memory and routing as primary design issues.
When you view LLM applications through this lens, the field becomes much easier to navigate. Real progress is found not only in the development of large models, but in the complex systems built around them. By mastering these building blocks, you're already thinking like an exceptional LLM engineer.
Kanwal Mehreen is a machine learning engineer and technical writer with a deep passion for data science and the intersection of AI and medicine. He co-authored the ebook “Increasing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, he strives for diversity and academic excellence. He has also been recognized as a Teradata Diversity in Tech Scholar, a Mitacs Globalink Research Scholar, and a Harvard WeCode Scholar. Kanwal is a passionate advocate for change, having founded FEMCodes to empower women in STEM fields.
