Prompt Engineering is optimization of prompts in language models (LMs) to build precise AImodels and in turn robust, innovative, future-forward applications.
Craft prompts skillfully to push the limits of GenAI and uncover stunning possibilities to enhanceyour decision-making capabilities.
Our Offerings
To better understand inputs and generate unbiased, ethical, and contextually accurate prompts, any organization requires technically supercharged GenAI experts. That is where, we at Nitor Infotech can play a crucial role, with:
Prompt Optimization
Automatically improve prompts for Large Language Models (LLMs), Language Models (LMs), ChatGPT, GPT 3.5 and Stable Diffusion models with Nitor Infotech.
Prompt Expansion
Be prompt-perfect! Add that extra level of detail, and tailor it to fit your user’s needs through theskills and knowledge of our GenAI experts.
Prompt Adaptation
Get more out of your AI models with less! Reduce the costs of your LLM API usage and shorten prompts through our Prompt Adaptation service.
Prompt Personalization
Craft prompts that power your models and workflows! With our Prompt personalization services you can easily generate human-like content capable of engaging in conversations and solving problems.
Prompt Refinement
Score an ace with specificity! Let our experts guide you into refining or fine-tuning your prompts to deliver clear and concise instructions for an amplified AI model performance.
Insights
Prompt Engineering POC
How can our GenAI-based dashboarding solution be theanswer to your query?
Better the performance of your AI models today!
Related Industries
Questions on your mind
What are some prompt engineering techniques?
Some prompt engineering techniques include -
- Zero-shot - It involves the language model making predictions without requiring explicit additional examples.
- N-shot - It's a spectrum of approaches where N symbolizes the number of cues or examples provided to the language model for generating predictions. ...
- Chain-of-Thought - It urges an AI model to explain intermediate reasoning stages before providing the final answer to a multi-stage problem.
- Self-consistency - It involves crafting diverse paths of reasoning and selecting highly consistent answers.
- Generated Knowledge - It uses a large language model to produce valuable information relevant to a prompt.
- Q & A - It allows crafting of thoughtful prompts so that language models can excel in tasks involving answering questions.
- Text Summarization - It guides language models to generate precise and accurate summaries of longer texts.
- Code Completion - It involves providing a partial code snippet to a language model that generates suggestions or completes the code based on the provided input.
- Code Generation - It assists in generating code snippets or programming solutions by providing clear task specifications.
How to write better prompts?
Here are some tips to craft better prompts-
- Be as specific as possible - Give specific prompts to get more accurate and focused responses.
- Describe your goal - Define exactly what kind of information you are looking for. ...
- Mention your setting and provide the context - Describe your goal, target audience and tone to get the most suited answers.
- Experiment with different prompt styles - Try different styles like asking to provide a summary, generate a list, or explain to a layman.
- Iterate/refine to get delve deeper or get better results - Refine or modify your questions to attain better results.
- Use your previous threads - Go back to specific discussions by using your previous threads.
- Ask open-ended questions - Ask open-ended questions to get comprehensive responses.
- Request examples - Ask for examples to better understand the concept or a particular topic.
Why is AI-based personalization important today?
AI-based personalization is an exciting development in today's digital landscape. It's crucial as it provides -
- Enhanced user experience
- Increased engagement and conversion ...
- Data-driven insights
- A competitive edge
- Efficient marketing and targeting
- Reduced information overload
- The building of brand loyalty
- Personalized learning and education