Prompt engineering
The way you phrase a prompt changes the answer. Clear, specific instructions with examples usually produce better results. Zero-shot prompts give no examples; few-shot prompts include a few input-output demonstrations.
Classify the sentiment of the following review as Positive or Negative.
Review: "The battery life is incredible and the screen is gorgeous."
Sentiment: Positive
Review: "It broke after two days and support never replied."
Sentiment: Negative
Review: "Good value for the price but the camera is weak."
Sentiment:Retrieval-Augmented Generation
LLMs do not know facts beyond their training data, and they can hallucinate. RAG grounds the model by retrieving relevant documents and including them in the prompt. The model then generates an answer conditioned on the retrieved text.
- Chunk documents into passages.
- Embed each chunk into a dense vector.
- Build a vector index for fast nearest-neighbor search.
- At query time, retrieve the top-k relevant chunks.
- Inject the chunks into the prompt and ask the model to answer.
Evaluation and benchmarks
Benchmarks measure different capabilities. Perplexity measures language modeling. MMLU tests knowledge across many subjects. HellaSwag tests commonsense reasoning. HumanEval tests code generation. TruthfulQA measures truthfulness.
Benchmarks are not users
A model can score well on a benchmark and still be frustrating in practice. Real-world evaluation includes latency, cost, safety, tone, and usefulness for your specific task.
Failure modes
LLMs are not reliable calculators, legal advisors, or search engines. They hallucinate facts, reproduce training data, reflect biases in their data, and can be jailbroken into producing harmful content.
- Hallucination: confident statements that are false or unsupported.
- Bias: stereotypes and skewed representations from training data.
- Jailbreaking: prompts that bypass safety guardrails.
- Overconfidence: fluent answers that sound correct but are not.
Latency, cost, and deployment
Running large models is expensive. Smaller models are faster and cheaper but less capable. Quantization reduces precision to fit models on smaller hardware. Caching, batching, and speculative decoding improve throughput.
For many applications, a fine-tuned 7B or 13B model is sufficient. For complex reasoning or coding, larger models still dominate. Choose the smallest model that meets your quality target.
Building reliable applications
Reliable LLM applications validate outputs, use structured generation, provide citations, and keep humans in the loop. Chain-of-thought prompting asks the model to show its reasoning, which can improve accuracy and make errors easier to catch.
Solve this step by step and explain your reasoning.
Question: A train leaves A at 60 km/h and another leaves B at 80 km/h. They are 420 km apart. When do they meet?Key takeaway
Using LLMs well requires prompt design, retrieval, evaluation, and awareness of failure modes. Match the model size to your task, measure real-world performance, and design systems that handle uncertainty.