Prompt Engineering

Named Entity Recognition (NER)

Identifying and categorizing key information (names, places, dates) in text. Example: "Stanford University is located in California" identifies "Stanford University" as an organization and "California" as a location.

Part-of-Speech Tagging (POS)

Identifying each word's part of speech (e.g., noun, verb, adjective). Example: In "The quick brown fox jumps", "jumps" is tagged as a verb (V).

Clarify / Rephrase

If an answer isn't clear, ask for clarification or a different wording. Example: "Can you rephrase that using simpler terms?"

Self-Supervised Learning

Learning to predict part of the input from other parts of the input without explicit external labels. Example: BERT learning word representations by predicting masked words in sentences.

AI Safety and Robustness (Ethical and Practical Considerations)

The study and practice of ensuring AI systems operate safely and predictably, especially as they pertain to preventing harmful outputs and ensuring the model's responses are aligned with ethical guidelines.

Tokenization (Model-Specific)

The process of converting text into smaller units (tokens), such as words or subwords, that can be processed by a model. Example: "I love NLP" becomes ["I", "love", "NLP"].

Fine-Tuning (Model-Specific)

The process of taking a pre-trained model and further training it on a smaller, task-specific dataset to adapt the model to a specific task or set of tasks.

Neural Machine Translation (NMT)

Using neural networks to translate text from one language to another. Example: Google's NMT system translating English to French.

GenAI QA Framework: Step 1. Define Objectives & Guidelines

Define the requirements for the AI prompt engineering outputs. Specify the desired outcomes, such as the target audience, language use, style, tone, and ethnical considerations. Define metrics for evaluating the prompt outputs.

Multimodal Models (Advanced Model Architecture and Theory)

AI models that can process and understand information from different data types, such as text, images, and audio, simultaneously. Example 1: Generating a descriptive caption for a photograph by understanding the image content and converting it into relevant text. Example 2: Answering questions about a video clip by analyzing both the visual and auditory information to provide a textual response.

Transformer Architecture (Model-Specific)

A neural network architecture that relies on self-attention mechanisms to process input data in parallel, significantly improving the efficiency and effectiveness of training deep learning models, especially for NLP tasks.

Temperature

A parameter in some language models that controls the randomness of predictions by scaling the logits before applying softmax. It affects the creativity and variability of the output.

Bag of Words (BoW)

A representation of text that describes the occurrence of words within a document, ignoring the order. Example: The sentence "The cat sat on the mat" would be represented as {the:2, cat:1, sat:1, on:1, mat:1}.

Long Short-Term Memory (LSTM)

A special kind of RNN capable of learning long-term dependencies. Example: LSTMs used in language modeling to predict subsequent text.

TF-IDF (Term Frequency-Inverse Document Frequency)

A statistic that reflects how important a word is to a document in a collection or corpus. Example: "quantum" might have a high TF-IDF score in a document about physics.

GenAI Consulting Major Talking Points

1. Return for my AI Investment? ROI is important for clients 2. What pain points have you never been able to solve? Addressing their bottlenecks to tackle their pain points (i.e., business value) 3. How are you increasing the quality of GenAI apps? 4. Is now time to take advantage of GenAI? Improvements. 5. Models to Agents to OS: Keeping up with pace of AI Innovation 6. How are you setting standard for using this technology?

Recurrent Neural Networks (RNN)

A class of neural networks for processing sequential data. Example: An RNN used for generating text character by character.

What is Active Learning ?

A strategy for guiding AI behavior and output. Definition: A learning approach where the model identifies gaps in its knowledge or areas of uncertainty and seeks out new data or human input to learn more effectively. Example of Use: An image recognition model that, when uncertain about classifying images into categories, requests human feedback to improve its accuracy over time. Prompt Keywords: "Identify which images are uncertain," "Request clarification on," "Learn from the most informative examples."

What is Reinforcement Learning?

A strategy for guiding AI behavior and output. Definition: An area of machine learning where an AI learns to make decisions by performing actions and receiving feedback in the form of rewards or penalties. Example of Use: An AI playing a video game where it learns the best strategies for winning by repeatedly playing the game and optimizing its actions for maximum score. Prompt Keywords: "Learn to maximize rewards," "Adjust actions based on feedback," "Strategy optimization through trial and error."

What is Explainable AI (XAI)?

A strategy for guiding AI behavior and output. Definition: Techniques and approaches designed to make the decision-making processes of AI models transparent and understandable to humans. Example of Use: A credit scoring AI that not only predicts whether a loan should be approved but also provides explanations for its decision based on factors like credit history, income, and debt levels. Prompt Keywords: "Explain your reasoning," "Justify your decision with evidence," "Provide the rationale behind."

What is Zero-Shot Learning?

A strategy for guiding AI behavior and output. Definition: The ability of a model to correctly perform tasks it has not been explicitly trained to do, using only its pre-existing knowledge. Example of Use: A language model generating accurate and relevant responses to questions about newly emerged technologies or events it was not trained on. Prompt Keywords: "Generate an answer based on what you know," "Infer with no prior examples," "Apply your general understanding to."

What is Transfer Learning?

A strategy for guiding AI behavior and output. Definition: Utilizing a pre-trained model on a large dataset and adapting it to a related but different task with minimal additional training. Example of Use: Adapting a model trained on general English text to perform specialized medical text analysis, such as identifying symptoms or diseases mentioned in patient reports. Prompt Keywords: "Adapt knowledge from general to specific," "Using what you know about language, analyze medical texts."

Machine Learning (ML)

A subset of artificial intelligence (AI) that focuses on building systems that learn from data. Machine learning algorithms use statistical techniques to enable computers to improve at tasks with experience.

Deep Learning

A subset of machine learning that uses neural networks with many layers (deep neural networks) to learn from large amounts of data. Deep learning is a key technology behind advanced NLP models.

BERT (Bidirectional Encoder Representations from Transformers)

A transformer-based model designed to understand the context of words in search queries or other text. Example: Improving search results based on the context of search queries.

Generative Pre-trained Transformer (GPT) (Model-Specific)

A type of large language model developed by OpenAI that uses deep learning to produce human-like text. It is pre-trained on a diverse internet corpus and can be fine-tuned for specific tasks.

Transformer Models

A type of model that uses self-attention to weigh the influence of different words on each other. Example: BERT, which can understand the context of words in text.

Vector Database

A vector database in the context of Generative AI (GenAI) is a specialized type of database designed to efficiently store, search, and manage vector embeddings. Vector embeddings are high-dimensional representations of data, often derived from text, images, audio, or other types of input using machine learning models, particularly those based on deep learning. These embeddings capture the semantic or conceptual similarities between inputs in a way that can be mathematically quantified, typically in a space where distances between vectors represent similarities or differences between the original inputs.

Problems Being Faced By Clients

Accuracy and reliability from data perspective (E.g., chunking)

Ethical Considerations in NLP

Addressing biases, fairness, and the societal impact of NLP technologies. Example: Analyzing and mitigating gender bias in word embeddings.

Fine-Tuning

Adjusting a pre-trained model slightly to adapt to a specific task. Example: Fine-tuning BERT for a sentiment analysis task.

Dynamic Prompting (Advanced Prompt Engineering Techniques)

Adjusting the prompts based on the model's previous outputs or external factors to guide the model towards a desired outcome. Example 1: If a model generates an unsatisfactory story ending, dynamically adjust the prompt to guide it towards a different conclusion. Example 2: Changing the prompt to ask for more detailed explanations if the initial model output is too vague or general.

Attention Mechanisms

Allows models to weigh the importance of different inputs dynamically. Example: In machine translation, focusing more on the current word being translated.

Conditional Requests

Asking for information under certain conditions. Example: "If it's raining, what are indoor activities to do in New York City?"

GenAI QA Framework: Step 2 - Establish A Review Team

Assemble a team of domain experts, linguists, ethicists, and Quality Assurance professionals. Ensure the team members have a deep understanding of the objectives, guidelines, and ethical considerations.

GenAI QA Framework: Step 4 - Evaluate Prompt Outputs

Assess the outputs to ensure that they meet the requirements, based on the prompt inputs. Assess the effectiveness, accuracy, and reliability of the responses. Ensure that the response align with the objectives and desired outcomes. Parse the response manually for potential issue areas (Concrete facts, dates, citations, material or ideas, that may be copy-written or plagiarized; bias).

What is one of four prompt engineering principles to make high-quality prompts?

Be detailed and specific. Provide detailed info about your desired output using direct language. Focus the prompt on a specific problem, topic, question, or output type. Multiple asks in a single prompt can make it difficult for a model to answer effectively.

GenAI QA Framework: Step 6 - Monitor Real-World Interactions

Collect feedback from users who interact with the AI system. Monitor the performance of the prompts in real-world scenarios. Identify any issues, errors, or biases in the prompt outputs reported by users. For each potential issue spotted: Ask for sourcing and manually examine the provided sources for accuracy and plagiarism. Do a separate search on the words used specifically to search for plagiarism.

Multimodal NLP

Combining text data with other types of data, like images or audio, for processing. Example: Generating image captions that describe the content of an image using both visual and textual information.

Stop Words

Common words that are often filtered out due to their lack of meaningful information. Example: "the", "is", "at", which are often removed in search queries.

Transformer Decoders and Encoders (Advanced Model Architecture and Theory)

Components of a transformer model where encoders process the input data, and decoders generate output. Encoders understand the context, and decoders use this understanding to produce predictions. Example 1: In language translation, the encoder processes the source language text, and the decoder generates the translated text in the target language. Example 2: For text completion tasks, the encoder reads the given text prompt, and the decoder generates the subsequent text based on the context provided by the encoder.

Data Privacy and Security (Ethical and Practical Considerations)

Concerns related to protecting sensitive information from unauthorized access or exposure during the training and deployment of AI models.

Societal Impact of Prompt-Driven Outputs (Ethical & Societal Implications)

Considering the broader effects of AI-generated content on society, including issues of misinformation, content authenticity, and ethical use. Example 1: Using prompts to generate news articles that include checks for factual accuracy and sources to combat misinformation. Example 2: Prompting models to create educational content that is fact-checked and includes disclaimers about AI involvement to ensure transparency and trust.

GenAI QA Framework: Step 7 - Establish Iterative Improvement

Continuously iterate and improve the prompts based on feedback and evaluation results. Document the lessons learned and update the guidelines accordingly. Collaborate with the prompt engineering team to refine and enhance the prompts over time.

Lemmatization

Converting words to their base or dictionary form. Example: "am", "are", "is" become "be".

Meta-Prompting (Advanced Prompt Engineering Techniques)

Crafting prompts that ask the model to analyze or reflect on its own responses or thought processes. Example 1: After generating a response, prompt the model to explain the reasoning behind its answer. Example 2: Prompt the model to identify potential biases in its previous response.

GenAI QA Framework: Step 5 - Design Test Scenarios

Create a variety of test scenarios that cover different use cases and potential edge cases. Test the prompt with these scenarios to evaluate the performance and accuracy of the AI system. Assess the ability of the prompts to handle multiple inputs.

The Modern AI Stack

Data: Enterprises generating significant amounts of data, whether internally or through customer interactions Vectors: Vectors are crucial for deriving value from data and are central to the functionality of leading AI applications in current use. Success: Success in AI is defined by an orgs proficiency in handling intricate data workflows and providing user experiences that exceed anticipated outcomes.

Self-Consistency

Definition: A decoding strategy where a language model generates multiple reasoning paths or answers to a given question and then selects the most coherent and consistent answer among them. This technique aims to enhance the accuracy and reliability of the model's outputs by comparing and evaluating different possible answers. Examples of Usage: Example 1: Historical Analysis: Prompt: "Explain the causes of the French Revolution and use the Self-Consistency method to ensure the explanation is accurate and coherent." Prompt Keywords: "causes of the French Revolution", "Self-Consistency method", "accurate and coherent explanation". Example 2: Problem Solving in Mathematics: Prompt: "Solve the quadratic equation x^2 - 5x + 6 = 0 using the Self-Consistency strategy to verify the correctness of the solution." Prompt Keywords: "solve quadratic equation", "x^2 - 5x + 6 = 0", "Self-Consistency strategy", "verify correctness". These examples and keywords are intended to guide the construction of prompts that effectively leverage the ReAct and Self-Consistency techniques. By tailoring the approach to the specific task or problem at hand, users can harness these methods to improve the depth, actionability, and reliability of the model's responses.

ReAct Prompting Technique

Definition: A prompting technique that integrates the "Chain of Thought" (CoT) approach with "Action Plan Generation". It first induces the model to think through a problem step-by-step (CoT) and then formulates an action plan to tackle the problem, essentially merging deep reasoning with actionable steps. This method is particularly useful for complex problems requiring both understanding and subsequent action. Examples of Usage: Example 1: Project Planning: Prompt: "Given the objective to launch a new product within 6 months, use the ReAct method to outline the necessary steps from market research to launch." Prompt Keywords: "launch new product", "6 months", "market research", "ReAct method", "outline steps". Example 2: Personal Fitness Goal: Prompt: "Using the ReAct technique, create a 3-month workout and diet plan for someone aiming to improve their overall fitness and lose weight." Prompt Keywords: "improve fitness", "lose weight", "3-month plan", "workout and diet", "ReAct technique".

Sentiment Analysis

Definition: Determining the sentiment expressed in a piece of text. Example: Classifying a product review as positive, negative, or neutral. Example: "Summarize the key points of the following text, then give it a sentiment rating on a scale of 0 to 100 for positivity, hate, and excitement: <input text>

What is Action Plan Generation?

Definition: This is a prompting technique that uses a language model to generate actions to take. The results of these actions can then be fed back into the language model to generate a subsequent action. It's like having a dynamic conversation with the model where it suggests actions and then reacts to the outcomes of those actions. This makes the model more interactive and responsive, capable of adapting to changing circumstances and requirements.

Word Embeddings

Dense vector representations of words that capture their meanings. Example: Word2Vec representations where similar words have similar embeddings.

Expectations of a Prompt Engineer (Roles & Responsibilities)

Develop the AI prompt engineering system: Design the system architecture, write the code, and test the system. Oversees the development and maintenance of prompt libraries and guidelines. Develop high-quality prompts: Train machine learning models with data scientists. Continuously improve prompts. Collaborate with cross-functional teams: Data scientists, software engineers, and product managers to ensure that machine learning models are integrated effectively into the company's products or services.

Transparency and Explainability in Prompt Engineering (Ethical & Societal Implications)

Developing prompts that help models generate outputs which are understandable and interpretable by humans. Example 1: Crafting prompts that ask the model to not only make a prediction but also provide the reasoning behind its prediction. Example 2: Designing prompts for a model to explain the steps it would take to solve a math problem, enhancing the educational value of the response.

Follow-up Questions

Dig deeper into a topic based on previous answers. Example: "You mentioned renewable energy. How does solar power work?"

QA approach to AI

Effectiveness, accuracy, reliability, and ethical soundness

Role Vector Database Perform

Efficient Similarity Search: They allow for the rapid retrieval of items that are semantically similar to a query item. For example, in a text-based application, a vector database can help find documents or sentences that are conceptually similar to a given piece of text. Scalability: They are optimized to handle the high dimensionality and volume of vector data generated by AI models, ensuring that search and retrieval operations remain efficient even as the dataset grows. Semantic Understanding: By leveraging the semantic information encoded in vector embeddings, vector databases support more nuanced and contextually aware operations compared to traditional databases that rely on exact matches or keyword-based searches. Flexibility: They can be applied across different types of data (text, images, audio) and for various applications, including recommendation systems, content discovery, anomaly detection, and more.

Algorithmic Fairness in Prompt Design (Ethical & Societal Implications)

Ensuring that prompts do not inadvertently introduce or perpetuate biases in the model's outputs. Example 1: Designing prompts for a job application screening tool that avoids language that could bias the model against certain demographic groups. Example 2: Creating prompts for a news summarization model that encourage balanced representation of different viewpoints.

GenAI QA Framework: Step 3 - Review Prompt Inputs

Evaluate the quality and diversity of the prompt inputs used for training the AI model. Check if the prompt inputs cover a wide range of scenarios and potential user inputs. Assess the relevance, clarity, and appropriateness of the prompt inputs.

Client Delivery: Retrieval Augmented Generation

Every LLM has been trained on the public internet. Vector DB has been trained on internet and out-perform an existing model. There are new models coming out for new jobs; specialization is important.

What is one of four prompt engineering principles to make high-quality prompts?

Experiment. Crafting effective prompts is more of an art than a science. Try different approaches to prompting.

Direct Instructions

Giving explicit instructions for specific tasks or outputs. Example: "Generate an email template for a job application follow-up."

Transformative AI companies

Industry-first clustering-based alogrithms that delivers low latency, always fresh data, and high recall at low cost for any scale.

Temporal Queries

Inquiring about events or information at specific times. Example: "What are the major tech trends in 2023?"

What is Role Prompting?

It is 1 of the 5 strategies used to create effective prompts. Definition: Ask the model to behave like a certain person, profession, or role. This gives it context for the types of answers the model should provide and can help to increase response accuracy and quality. Example: Prompt: "You are a motivational coach. Give me advice on staying motivated while working from home." This instructs the AI to adopt the role of a motivational coach, tailoring its response to be encouraging and supportive. Example 2: Act like you are a teacher that uses metaphors/similes to simplify complicated concepts to a non-technical aduiecne. Explain large language models to me. Usage: This strategy is useful for tasks that require the model to output using a specific language or syntax. By defining the model's role, role prompting enhances the relevance and accuracy of generated outputs for various applications. Prompt: "from the perspective of..."

What is Generated Knowledge Prompting?

It is 1 of the 5 strategies used to create effective prompts. Definition: Generated knowledge prompting involves asking the AI to create or infer new information based on its training data. This can include generating ideas, predictions, or explanations that aren't directly found in the data but are instead derived through reasoning or creative thinking. Example 1: Prompt: "What could be the potential benefits of teleportation technology in the future?"The AI is prompted to generate insights or predictions about the benefits of a hypothetical technology. Example 2: Use prior information provided to execute a subsequent prompt.

What is One-Shot / Few-Shot Learning (Prompting)?

It is 1 of the 5 strategies used to create effective prompts. Definition: One-shot and few-shot prompting refer to techniques where the prompt includes one or a few examples of the desired output format or content. This helps the model understand the task and how to structure its response. One-shot uses a single example, while few-shot provides several examples. Example (One-shot): Prompt: "Question: What is photosynthesis? Answer: Photosynthesis is the process by which green plants and some other organisms use sunlight to synthesize foods from carbon dioxide and water. Question: What is the capital of France?"The model is given an example of a Q&A format to follow for its response. Example of Use: Training a model to recognize new categories of images with only a few examples per category. For instance, teaching an AI to differentiate between different types of leaves from just a handful of images of each type. Prompt Keywords: "Given these examples," "Identify which category this belongs to," "Similar to the following examples."

What is Adding Prompt Modifiers?

It is 1 of the 5 strategies used to create effective prompts. Definition: Prompt modifiers are additional instructions or constraints added to the prompt to refine the AI model's output. These can include tone, style, format, or specific content to include or avoid. Modifiers help steer the response in a desired direction, ensuring that the output meets particular requirements or preferences. Example: Prompt: "Explain quantum computing in simple terms, as if you're talking to a 10-year-old."Here, "in simple terms, as if you're talking to a 10-year-old" acts as a modifier, guiding the model to simplify its explanation.

What is Chain of Thought (CoT) Prompting?

It is 1 of the 5 strategies used to create effective prompts. This is also a key concept of LangChain. Definition: Chain of thought prompting encourages the AI to break down its reasoning process into intermediate steps before reaching a conclusion or answer. This technique can improve the model's ability to tackle complex problems or questions by making its thought process explicit. Example: Prompt: "To solve the math problem 'If I have 3 apples and you give me 2 more, how many apples do I have in total?', first add the number of apples I had to the number you gave me. Then, provide the total. "This instructs the AI to explicitly describe the steps (chain of thought) it takes to arrive at the answer. Example of Use: Solving complex arithmetic problems by instructing the AI to outline each step of its calculation process before presenting the final answer. Prompt Keywords: "Show your work step by step," "First, then, finally," "Outline the reasoning process."

What is the most important of the 4 engineering principles to make high-quality prompts?

Know Your Model: Different language models have different capabilities, strengths, and weaknesses. Some models are not trained on code, not trained to perform well with complicated reasoning, or not trained on recent data. This is the most important principle of crafting effective prompts.

What is LangChain (Framework and Collection of Tools)?

LangChain is a framework and collection of tools designed to facilitate the development and deployment of language model applications, particularly those that interact with external knowledge sources or perform complex reasoning tasks. It was developed by Michael Matena and emphasizes creating applications that can effectively utilize language models like GPT (Generative Pre-trained Transformer) for a wide range of tasks, including but not limited to chatbots, automated research, and content generation.

Issues in AI

Large evolving data volume, data is multimodal user behavior is dynamic and unpredictable

Prompt Design Strategies

Methods used in prompt engineering, such as including examples, specifying output format, and using clarifying statements to guide the model's output.

Language Models

Models that can predict the likelihood of a sequence of words. Example: GPT-3 generating human-like text based on a prompt.

Sequence Models

Models that predict the next item in a sequence. Example: Using RNNs to predict the next word in a sentence.

Blob Storage

New Arch. Upsert / Delete goes to Log writer and then Blob Storage containing 2 payloads and 2 clusters, payloads interact with clusters, they all go to the index builder, and the log writer also goes to the freshness layer query to request router and to query planner and then to the query worker containing query execution that points to the freshness player. it interacts with the blob storage.

Embeddings (Advanced Model Architecture and Theory)

Numerical representations of text in a high-dimensional space, allowing models to understand semantic similarity between words or phrases. Example 1: Word embeddings can help in sentiment analysis by understanding that "good" and "excellent" have similar meanings. Example 2: Document embeddings can be used to recommend similar articles based on the content's overall theme and context.

Prompt Accuracy Guardrails

Step-by-Step Walkthrough: "Write a SQL query that <complicated task> using <schema>. Let's think step by step to be sure we get the correct answer"

Prompt Tuning (Soft Prompting) (Optimization & Evaluation)

Prompt Tuning (Soft Prompting): Optimizing a set of tunable parameters (soft prompts) to instruct the model on performing specific tasks, without modifying the model's pre-trained weights. Example 1: Fine-tuning a model to generate medical information summaries by optimizing soft prompts for clarity and conciseness in medical language. Example 2: Adapting a model for legal document analysis by tuning prompts to highlight relevant legal precedents and terminology.

What is one of four prompt engineering principles to make high-quality prompts?

Provide context. Contextual info helps the model to solve the task and generate more appropriate responses. Include conversation history, examples of the desired output, or the model's role in the prompt.

Feedback Loop

Providing feedback on the responses to refine the output. Example: "Your explanation was too technical. Can you simplify it?"

Stemming

Reducing words to their root form. Example: "fishing", "fished", "fisher" all stem to "fish".

Bias in AI (Ethical and Practical Considerations)

Refers to the prejudice or unfairness in the outputs of AI models, often reflecting biases present in the training data.

AI application Landscape: Raw Data + AI Model

Search: Semantic Search, Product Search, Multi-Modal search, question-answering; Generation: Chatbots, Text generation Personalization: Recommendations, feed ranking, ad targeting, Analytics & ML: Data labeling, model training, molecular search, Security: Anomaly Detection, Fraud Detection, Bot/Threat Detection, Identity Verification, Data Management: Pattern Making, deduplication, grouping, tagging

ELI5 (Explain Like I'm 5)

Simplifies explanations for complex concepts. Example: "ELI5 how the stock market works.

Adversarial Examples in NLP

Specially crafted inputs designed to confuse NLP models into making mistakes. Example: Slightly modifying a text snippet in a way that causes a sentiment analysis model to misclassify it.

Prompt Evaluation Metrics (Optimization & Evaluation) (Ethical & Societal Implications)

Standards or measures used to assess the effectiveness of prompts in achieving desired responses from a model. Example 1: Evaluating the creativity of a story generated by a model using a prompt by comparing it to a benchmark of human-generated stories. Example 2: Measuring the accuracy of factual answers provided by the model in response to quiz prompts.

Expectations of a Prompt Engineer (Technical & SoftSkills)

Strong knowledge of several model development techniques: A prompt engineer should have a strong understanding of natural language processing, supervised and unsupervised learning, and deep learning. Proficiency in programming languages: Programming languages such as Python, Java, or C++ is essential for developing and optimizing prompts. Strong problem-solving skills: Strong problem-solving skills to identify issues with prompts and develop effective solutions. Excellent communication skills: Communicate effectively with cross-functional teams, including data scientists, software engineers, and product managers. Knowledge of natural language processing: Strong understanding of natural language processing techniques, including text preprocessing, text classification, and sentiment analysis

Prompt Templates (Advanced Prompt Engineering Techniques)

Structured frameworks for generating prompts that can be dynamically filled or adjusted based on specific task requirements. Example 1: Creating a template for product descriptions that can be adapted by inserting different product names and features. Example 2: A feedback request template that can be customized with specific aspects of a project or service for evaluation.

Explainable AI (XAI)

Techniques and methods that provide human-understandable explanations of machine learning model decisions. Example: Feature importance scores in NLP models explaining why a certain decision was made.

Natural Language Processing (NLP):

The field of computer science focused on the interaction between computers and humans through natural language. It involves enabling computers to understand, interpret, and generate human language in a valuable way. Example: Spell check in word processors.

Automatic Prompt Generation (Optimization & Evaluation)

The use of algorithms to create effective prompts automatically, optimizing the interaction with the model for specific outcomes. Example 1: Automatically generating quiz questions from educational content using an algorithm that identifies key facts and figures. Example 2: Using a model to generate interview questions based on a job description, ensuring they are relevant and comprehensive.

Attention Mechanisms (Advanced Model Architecture and Theory)

These mechanisms enable the model to focus on relevant parts of the input data when generating an output, improving the model's understanding and generation capabilities. Example 1: In machine translation, the model uses attention to focus on the relevant words in the source sentence when translating a specific word. Example 2: For summarization tasks, attention helps the model identify key information in a lengthy article to generate a concise summary.

Cross-Lingual Language Modeling (XLM)

Training language models that can understand and generate text across multiple languages. Example: Facebook's XLM, which enhances cross-lingual understanding.

Prompt Chaining (Advanced Prompt Engineering Techniques)

Using the output of one prompt as the input for another, enabling complex, multi-step reasoning or task completion. Example 1: First, prompt the model to summarize an article, then use that summary as a prompt to generate questions about the article's content. Example 2: Prompting the model to write a story, then using that story as a prompt to generate a moral or lesson learned from the narrative.

Traditional Vector DB Arch

Vectors sent to a writer then go to consistent hashing then broken out into 4 shards each shard needs a CPU and an Index, within the Index there is RAM and SSD needed. Query Vector uses query router to scatter / gather among the shards.

Client Delivery: Enhanced Data Classification

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Client Delivery: Question Answering

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Client Delivery: Semantic Search

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