How can I improve my LLM performance?
Asked by: Nicole Kling | Last update: August 20, 2025Score: 4.7/5 (36 votes)
- Optimizing LLM Training: ...
- Text Chunking and Embeddings: ...
- Optimizing Vectorization and Indexing: ...
- Caching and Memoization: ...
- Parallel Processing: ...
- Hardware Acceleration: ...
- Monitoring and Profiling: ...
- Experiment with Different Models:
How do you optimize LLM performance?
Optimize LLM performance and scalability using techniques like prompt engineering, retrieval augmentation, fine-tuning, model pruning, quantization, distillation, load balancing, sharding, and caching. Large language models or LLMs have emerged as a driving catalyst in natural language processing.
How can LLM accuracy be improved?
To improve model accuracy on a specific task: Training the model on task-specific data to solve a learned memory problem by showing it many examples of that task being performed correctly. To improve model efficiency: Achieve the same accuracy for less tokens or by using a smaller model.
How to improve LLM prompt performance?
The more detail you provide about context, length, format, and style, the better. This prevents misinterpretation and ensures usable output. Try few-shot prompting. This technique enables in-context learning where specific format examples are provided in the prompt to steer the LLM to better performance.
How to make LLM results more consistent?
Using methods like clear separators, breaking tasks into smaller steps, few-shot prompting, and chain-of-thought prompting can greatly improve the quality of LLM responses. Additionally, finding the right balance in prompt length and refining prompts through repeated adjustments are key steps to maximizing performance.
How to Improve your LLM? Find the Best & Cheapest Solution
What practice would help reduce hallucinations in an LLM advice?
Advanced prompting methods
By forcing the model to articulate a clear reasoning path, chain-of-thought prompts can reduce errors or hallucinations that might occur if the model were to jump directly to a conclusion without showing its work. Another popular technique is the few-shot prompting.
What is self consistency in LLM?
Self-Consistency Prompting is a prompt engineering method that enhances the reasoning capabilities of Large Language Models (LLMs) by generating multiple outputs and selecting the most consistent answer among them. This approach leverages the idea that complex problems can be approached in various ways.
How to make an LLM response faster?
Make fewer requests
If you have sequential steps for the LLM to perform, instead of firing off one request per step consider putting them in a single prompt and getting them all in a single response. You'll avoid the additional round-trip latency, and potentially also reduce complexity of processing multiple responses.
How do you train an LLM?
- Step 1: Define Your Goals.
- Step 2: Collect and Prepare Your Data.
- Step 3: Set Up the Environment.
- Step 4: Choose Model Architecture.
- Step 5: Tokenize Your Data.
- Step 6: Train the Model.
- Step 7: Evaluate and Fine-tune the Model.
- Step 8: Implementation of LLM.
How to write better LLM prompts?
Writing good LLM prompts requires you to:
Provide context when possible. Ask open-ended questions for an explanation. Ask for examples. Avoid ambiguity.
How to measure LLM accuracy?
LLM accuracy is typically measured using a few different metrics that assess how close the model's output is to a correct or expected response. Common metrics include precision, which shows how often the model's positive outputs are correct, and recall, which measures its ability to find all relevant correct answers.
Is ChatGPT LLM?
Yes, ChatGPT belongs to the LLM family because of the number of features it shares. Let's take a look at what binds them: Transformer architecture — LLM and ChatGPT models are constructed using the transformer architecture, which has demonstrated remarkable success in natural language processing tasks.
How can I make my accuracy better?
The best way to improve accuracy is to do the following: Read text and dictate it in any document. This can be any text, such as a newspaper article. Make corrections to the text by voice.
How to improve LLM accuracy?
Go through an iterative process of generating training data and fine-tuning, learning practical tips such as adding examples, generating variations, and filtering generated data to increase model accuracy.
How do you achieve performance optimization?
From a performance optimization standpoint, having an effective and elaborate caching mechanism is the first step that needs to be taken. Take the inventory of all key application components and pages and come up with a strategy to cache the data or computation that speeds up the overall process.
How to increase LLM inference speed?
To enhance LLM inference performance, you can use specialized hardware accelerators (for example, GPUs and TPUs) and optimized serving frameworks. You can apply one or more of the following best practices to reduce LLM workload latency while improving throughput and cost-efficiency: Quantization. Tensor parallelism.
How to develop LLM?
- Plan and code all the parts of an LLM.
- Prepare a dataset suitable for LLM training.
- Fine-tune LLMs for text classification and with your own data.
- Use human feedback to ensure your LLM follows instructions.
How much time to train LLM?
The training process for every model will be different – so there is no set amount of time taken to train an LLM. The amount of training time will depend on a few key factors: The complexity of the desired use case. The amount, complexity, and quality of available training data.
How to train LLM on own data?
- Choosing a pre-trained LLM.
- Setting up the environment.
- Fine-tuning the model.
- Monitoring and evaluating.
- Testing the model.
- Deploying and using a model.
How to optimize LLMs?
LLM Prompt Optimization – Prompt optimization involves crafting effective prompts or inputs to LLMs to receive desired outputs or responses. This could include experimenting with different prompt formats, lengths, or structures to achieve better performance or accuracy for specific tasks or domains.
How are LLM trained?
LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. The largest and most capable LLMs are generative pretrained transformers (GPTs). Modern models can be fine-tuned for specific tasks or guided by prompt engineering.
What is the QAG score in LLM?
QAG Score. QAG (Question Answer Generation) Score is a scorer that leverages LLMs' high reasoning capabilities to reliably evaluate LLM outputs. It uses answers (usually either a 'yes' or 'no') to close-ended questions (which can be generated or preset) to compute a final metric score.
What are the emergent behaviors of LLM?
Emergence can be defined as the sudden appearance of novel behavior. Large Language Models apparently display emergence by suddenly gaining new abilities as they grow.
What is the purpose of least to most prompting in LLMs?
Least-to-most prompting is a prompt engineering method that increases the problem-solving capabilities of LLMs by breaking down complex problems into a series of simpler subproblems that get executed sequentially.
What is self-reflection in LLM?
Also similar to humans, LLM agents can be instructed to reflect on their own CoT. This allows them to identify errors, explain the cause of these errors, and generate advice to avoid making similar types of errors in the future [10–15].