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| # import streamlit as st | |
| # | |
| # # @st.cache_resource | |
| # # def init_PE(): | |
| # # data = {"Persona Pattern":{"Intuition":"","Format":2,"Examples":3 }, | |
| # # "Question Refinement":{"Intuition":,"Format":,"Examples": }, | |
| # # "Cognitive Verifier":{"Intuition":,"Format":,"Examples": }, | |
| # # "Audience Persona":{"Intuition":,"Format":,"Examples": }, | |
| # # "Flipped Interaction":{"Intuition":,"Format":,"Examples": }, | |
| # # "Few-Shot Prompting":{"Intuition":,"Format":,"Examples": }, | |
| # # "Chain-Of-Thought":{"Intuition":,"Format":,"Examples": }, | |
| # # "Reason+Act Prompting":{"Intuition":,"Format":,"Examples": }, | |
| # # "Gameplay Pattern":{"Intuition":,"Format":,"Examples": }, | |
| # # "Template Pattern":{"Intuition":,"Format":,"Examples": }, | |
| # # "Meta Language":{"Intuition":,"Format":,"Examples": }, | |
| # # "Alternative Approach":{"Intuition":,"Format":,"Examples": }, | |
| # # "Outline Expansion":{"Intuition":,"Format":,"Examples": }} | |
| # st.title("Prompt Engineering Techniques") | |
| # st.markdown( | |
| # """ | |
| # Welcome to the prompt engineering section! Here | |
| # we aim to introduce some common techniques used to generate | |
| # consistent outputs from large language models like GPT and LlaMA. | |
| # """ | |
| # ) | |
| # PE_options = st.multiselect("Select which Prompt Engineering options you are interested in",["Persona Pattern","Question Refinement","Cognitive Verifier", | |
| # "Audience Persona","Flipped Interaction", "Few-Shot Prompting","Chain-Of-Thought", | |
| # "Reason+Act Prompting","Gameplay Pattern","Template Prompting", | |
| # "Meta Language Creation","Recipe Prompting","Alternative Approach", | |
| # "Outline Expansion"]) | |
| # c11,c21 = st.columns([1,1]) | |
| # with c11: | |
| # st.markdown( | |
| # """ | |
| # ### Persona Pattern | |
| # """ | |
| # ) | |
| # with st.expander("View Explanation and Examples"): | |
| # st.markdown( | |
| # """ | |
| # **<span style="color: #AEC6CF;">1. Intuition </span>** | |
| # <span style="color: lightgray;">Tell the LLM to act as a specific person,object or entity and then | |
| # provide them with a relevant task.</span> | |
| # **<span style="color: #AEC6CF;">2. Format </span>** | |
| # <span style="color: lightgray;">"Act as entity X, perform task Y."</span> | |
| # **<span style="color: #AEC6CF;">3. Example</span>** | |
| # <span style="color: lightgray;">"Act as a expert AI Engineer, explain to me how back-propagation works."</span> | |
| # """, | |
| # unsafe_allow_html=True | |
| # ) | |
| # | |
| # with c21: | |
| # st.markdown( | |
| # """ | |
| # ### Question Refinement | |
| # """ | |
| # ) | |
| # with st.expander("View Explanation and Examples"): | |
| # st.markdown( | |
| # """ | |
| # **<span style="color: #AEC6CF;">1. Intuition </span>**<br> | |
| # <span style="color: lightgray;">To ensure that the LLM can suggest | |
| # potentially better or more refined questions for the user to ask.</span><br> | |
| # **<span style="color: #AEC6CF;">2. Format </span>**<br> | |
| # <span style="color: lightgray;">"Whenever I ask a question, suggest a | |
| # better question and ask me if I would like to use it instead.</span><br> | |
| # **<span style="color: #AEC6CF;">3. Example</span>**<br> | |
| # <span style="color: lightgray;">Just enter this prompt into the chat before asking your question</span><br> | |
| # """, | |
| # unsafe_allow_html=True | |
| # | |
| # ) | |
| # c21,c22 = st.columns([1,1]) | |
| # with c21: | |
| # st.markdown( | |
| # """ | |
| # ### Cognitive Verifier | |
| # """ | |
| # ) | |
| # with st.expander("View Explanations and Examples"): | |
| # st.markdown( | |
| # """ | |
| # **<span style="color: #AEC6CF;">1. Intuition </span>** | |
| # <span style="color: lightgray;">To force the LLM to subdivide the original question | |
| # into multiple questions and use the answers to all those questions to fit into the final answer</span> | |
| # **<span style="color: #AEC6CF;">2. Format </span>** | |
| # <span style="color: lightgray;">"When you are asked a question,follow these rules:<br>Generate a number of additional questions that would help more accurately answer the question.<br> | |
| # Combine the answers to the individual questions to produce the final answer to the overall question."</span> | |
| # **<span style="color: #AEC6CF;">3. Example</span>** | |
| # <span style="color: lightgray;">Just enter this prompt into the chat before asking your question.</span> | |
| # """, | |
| # unsafe_allow_html=True | |
| # ) | |
| # | |
| # with c22: | |
| # st.markdown( | |
| # """ | |
| # ### Audience Persona | |
| # """ | |
| # ) | |
| # with st.expander("View Explanations and Examples"): | |
| # st.markdown( | |
| # """ | |
| # **<span style="color: #AEC6CF;">1. Intuition </span>**<br> | |
| # <span style="color: lightgray;">Intuition is for the LLM to know your level of | |
| # expertise so that it can better tailor it's answers to your needs</span><br> | |
| # **<span style="color: #AEC6CF;">2. Format </span>**<br> | |
| # <span style="color: lightgray;">"Explain X to me. Assume I am persona Y."</span><br> | |
| # **<span style="color: #AEC6CF;">3. Example</span>**<br> | |
| # <span style="color: lightgray;">"Explain the self-attention mechanism to me. | |
| # Assume I am a beginner with 0 knowledge."</span> | |
| # """, | |
| # unsafe_allow_html=True | |
| # ) | |
| # | |
| # c31,c32 = st.columns([1,1]) | |
| # with c31: | |
| # st.markdown( | |
| # """ | |
| # ### Flipped Interaction | |
| # """ | |
| # ) | |
| # with st.expander("View Explanations and Examples"): | |
| # st.markdown( | |
| # """ | |
| # **<span style="color: #AEC6CF;">1. Intuition </span>**<br> | |
| # <span style="color: lightgray;">For the LLM to be like a diagnoser and ask the | |
| # user questions until a result is met.</span><br> | |
| # **<span style="color: #AEC6CF;">2. Format </span>**<br> | |
| # <span style="color: lightgray;">"I would like you to ask me questions to help me | |
| # achieve X."</span><br> | |
| # **<span style="color: #AEC6CF;">3. Example</span>**<br> | |
| # <span style="color: lightgray;">"I would like you to ask me questions to help me create variations of my marketing materials. You should ask questions until you have sufficient information about my | |
| # current draft messages, audience, and goals. Ask me the first question."</span><br> | |
| # """, | |
| # unsafe_allow_html=True | |
| # ) | |
| # | |
| # with c32: | |
| # st.markdown( | |
| # """ | |
| # ### Few-Shot Prompting | |
| # """ | |
| # ) | |
| # with st.expander("View Explanations and Examples"): | |
| # st.markdown( | |
| # """ | |
| # **<span style="color: #AEC6CF;">1. Intuition </span>**<br> | |
| # <span style="color: lightgray;">To give the LLM some input and output | |
| # pairs and hopefully it can learn and generate the expected output | |
| # from given input. Very similar to conventional training paradigm minus | |
| # weight updates.</span><br> | |
| # **<span style="color: #AEC6CF;">2. Format </span>**<br> | |
| # <span style="color: lightgray;">"Input:X, Output: Y"(Simple)<br> | |
| # "Situation:X, Think_1:Y,Action_1:Z,Think_2: A, Action_2: B"(Step by step)</span><br> | |
| # **<span style="color: #AEC6CF;">3. Example</span>**<br> | |
| # <span style="color: lightgray;">"1. Review: "The film was absolutely fantastic, with a gripping storyline and excellent acting." | |
| # Classification: Positive <br> 2. Review: "I found the movie to be a complete waste of time, with poor plot development and bad acting." | |
| # Classification: Negative"</span><br> | |
| # """, | |
| # unsafe_allow_html=True | |
| # ) | |
| # | |
| # c41,c42 = st.columns([1,1]) | |
| # | |
| # with c41: | |
| # st.markdown( | |
| # """ | |
| # ### Chain-Of-Thought | |
| # """ | |
| # ) | |
| # with st.expander("View Explanations and Examples"): | |
| # st.markdown( | |
| # """ | |
| # **<span style="color: #AEC6CF;">1. Intuition </span>**<br> | |
| # <span style="color: lightgray;">Similar to few-shot prompting except | |
| # now we provide reasoning for the output as well.</span><br> | |
| # **<span style="color: #AEC6CF;">2. Format </span>**<br> | |
| # <span style="color: lightgray;">"Question, step by step solution and then solution"</span><br> | |
| # **<span style="color: #AEC6CF;">3. Example</span>**<br> | |
| # <span style="color: lightgray;">"Solve the following problem step-by-step: | |
| # | |
| # Problem: What is the result of 8 multiplied by 7 plus 6? | |
| # | |
| # Step-by-step solution: | |
| # 1. First, calculate the multiplication: 8 * 7 = 56 | |
| # 2. Then, add 6 to the result: 56 + 6 = 62 | |
| # | |
| # Answer: 62"</span><br> | |
| # """, | |
| # unsafe_allow_html=True | |
| # ) | |
| # | |
| # with c42: | |
| # st.markdown( | |
| # """ | |
| # ### Reason+Act Prompting | |
| # """ | |
| # ) | |
| # with st.expander("View Explanations and Examples"): | |
| # st.markdown( | |
| # """ | |
| # **<span style="color: #AEC6CF;">1. Intuition </span>**<br> | |
| # <span style="color: lightgray;">Very similar to Chain-Of-Thought except now | |
| # we also teach the LLM to use external tools to get information.</span><br> | |
| # **<span style="color: #AEC6CF;">2. Format </span>**<br> | |
| # <span style="color: lightgray;">"Question,Think,Action,Result"</span><br> | |
| # **<span style="color: #AEC6CF;">3. Example</span>**<br> | |
| # <span style="color: lightgray;">"**Question:** Aside from the apple remote, what other | |
| # device can control the program apple remote? <br> | |
| # **Task:** I need to find out search Apple Remote and find out the devices it was originally programmed to interact with<br> | |
| # **Action:** Search [Apple Remote](https://en.wikipedia.org/wiki/Apple_Remote_Desktop)<br> | |
| # **Result:** Apple Remote Desktop (ARD) is a Macintosh application produced by Apple Inc., first released on March 14, 2002, that replaced a similar product called Apple Network Assistant"</span><br> | |
| # """, | |
| # unsafe_allow_html=True | |
| # ) | |
| # | |
| # c51,c52 = st.columns([1,1]) | |
| # with c51: | |
| # st.markdown( | |
| # """ | |
| # ### Gameplay Pattern | |
| # """ | |
| # ) | |
| # with st.expander("View Explanations and Examples"): | |
| # st.markdown( | |
| # """ | |
| # **<span style="color: #AEC6CF;">1. Intuition </span>**<br> | |
| # <span style="color: lightgray;">Imagine you want to learn about a new topic | |
| # , you can format it as a game and play with the LLM.</span><br> | |
| # **<span style="color: #AEC6CF;">2. Format </span>**<br> | |
| # <span style="color: lightgray;">Create a game about X topic. Describe the rules.</span><br> | |
| # **<span style="color: #AEC6CF;">3. Example</span>**<br> | |
| # <span style="color: lightgray;">"Create a game about prompt engineering. Ask me a series of questions | |
| # about prompt engineering and see how many I get right. If I get it wrong correct me."</span><br> | |
| # """, | |
| # unsafe_allow_html=True | |
| # ) | |
| # | |
| # with c52: | |
| # st.markdown( | |
| # """ | |
| # ### Template Prompting | |
| # """ | |
| # ) | |
| # with st.expander("View Explanations and Examples"): | |
| # st.markdown( | |
| # """ | |
| # **<span style="color: #AEC6CF;">1. Intuition </span>**<br> | |
| # <span style="color: lightgray;">Basically give a template of the output that you want the | |
| # LLM to follow. </span><br> | |
| # **<span style="color: #AEC6CF;">2. Format </span>**<br> | |
| # <span style="color: lightgray;">Task, template </span><br> | |
| # **<span style="color: #AEC6CF;">3. Example</span>**<br> | |
| # <span style="color: lightgray;">"Create a random strength workout for me today with complementary exercises. I am going to provide a template for your output . CAPITALIZED WORDS are my placeholders for content. Try to fit the output into one or more of the placeholders that I list. | |
| # Please preserve the formatting and overall template that I provide. | |
| # This is the template: NAME, REPS @ SETS, MUSCLE GROUPS WORKED, DIFFICULTY SCALE 1-5, FORM NOTES"</span><br> | |
| # """, | |
| # unsafe_allow_html=True | |
| # ) | |
| # | |
| # c61,c62 = st.columns([1,1]) | |
| # | |
| # with c61: | |
| # st.markdown( | |
| # """ | |
| # ### Meta Language Creation | |
| # """ | |
| # ) | |
| # with st.expander("View Explanations and Examples"): | |
| # st.markdown( | |
| # """ | |
| # **<span style="color: #AEC6CF;">1. Intuition </span>**<br> | |
| # <span style="color: lightgray;">Basically teaching the LLM your own | |
| # secret language like when you say X you mean Y.</span><br> | |
| # **<span style="color: #AEC6CF;">2. Format </span>**<br> | |
| # <span style="color: lightgray;">"When I say X, I mean Y (or would like you to do Y)"</span><br> | |
| # **<span style="color: #AEC6CF;">3. Example</span>**<br> | |
| # <span style="color: lightgray;">"When I say "variations of companies", I mean give me ten different variations of tech companies"</span><br> | |
| # """ | |
| # ) | |
| # | |
| # with c62: | |
| # st.markdown( | |
| # """ | |
| # ### Recipe Prompting | |
| # """ | |
| # ) | |
| # with st.expander("View Explanations and Examples"): | |
| # st.markdown( | |
| # """ | |
| # **<span style="color: #AEC6CF;">1. Intuition </span>**<br> | |
| # <span style="color: lightgray;">Asking a question to the LLM and also | |
| # letting it know that there exists intermediate steps required.</span><br> | |
| # **<span style="color: #AEC6CF;">2. Format </span>**<br> | |
| # <span style="color: lightgray;">I would like to achieve X. | |
| # I know that I need to perform steps A,B,C. Please provide me with the | |
| # necessary steps as well as any missing steps.</span><br> | |
| # **<span style="color: #AEC6CF;">3. Example</span>**<br> | |
| # <span style="color: lightgray;">"I would like to drive to MBS from Jurong. I know that I want to go through AYR and I don't want to drive more than 300 miles per day. | |
| # Provide a complete sequence of steps for me. Fill in any missing steps."</span><br> | |
| # """, | |
| # unsafe_allow_html=True | |
| # ) | |
| # | |
| # c71,c72 = st.columns([1,1]) | |
| # | |
| # with c71: | |
| # st.markdown( | |
| # """ | |
| # ### Alternative Approach | |
| # """ | |
| # ) | |
| # with st.expander("View Explanations and Examples"): | |
| # st.markdown( | |
| # """ | |
| # **<span style="color: #AEC6CF;">1. Intuition </span>**<br> | |
| # <span style="color: lightgray;">To allow the LLM to return users | |
| # alternative approaches to solve a problem.</span><br> | |
| # **<span style="color: #AEC6CF;">2. Format </span>**<br> | |
| # <span style="color: lightgray;"></span>"If there are alternative ways to accomplish a task X that I give you, list the best alternate approaches "<br> | |
| # **<span style="color: #AEC6CF;">3. Example</span>**<br> | |
| # <span style="color: lightgray;">Just input the above prompt before asking your question or task.</span><br> | |
| # """, | |
| # unsafe_allow_html=True | |
| # ) | |
| # | |
| # with c72: | |
| # st.markdown( | |
| # """ | |
| # ### Outline Expansion | |
| # """ | |
| # ) | |
| # with st.expander("View Explanations and Examples"): | |
| # st.markdown( | |
| # """ | |
| # **<span style="color: #AEC6CF;">1. Intuition </span>**<br> | |
| # <span style="color: lightgray;">Give the LLM a topic to provide | |
| # an outline on and then proceed to ask it to expand on a certain part.</span><br> | |
| # **<span style="color: #AEC6CF;">2. Format </span>**<br> | |
| # <span style="color: lightgray;">"Act as an outline expander. | |
| # Generate a bullet point outline based on the input that I give you and then ask me for which bullet point you should expand on. | |
| # Create a new outline for the bullet point that I select. | |
| # At the end, ask me for what bullet point to expand next."</span><br> | |
| # **<span style="color: #AEC6CF;">3. Example</span>**<br> | |
| # <span style="color: lightgray;">"Act as an outline expander. Generate a bullet point outline based on the input that I give you and then ask me for which bullet point you should expand on. Each bullet can have at most 3-5 sub bullets. The bullets should be numbered using the pattern [A-Z].[i-v].[* through ****]. Create a new outline for the bullet point that I select. At the end, ask me for what bullet point to expand next. Ask me for what to outline."</span><br> | |
| # """, | |
| # unsafe_allow_html=True | |
| # ) | |
| # | |