Johann Buscail johannbuscail

You probably don't know how to do Prompt Engineering

(This post could also be titled "Features missing from most LLM front-ends that should exist")

Apologies for the snarky title, but there has been a huge amount of discussion around so called "Prompt Engineering" these past few months on all kinds of platforms. Much of it is coming from individuals who are peddling around an awful lot of "Prompting" and very little "Engineering".

Most of these discussions are little more than users finding that writing more creative and complicated prompts can help them solve a task that a more simple prompt was unable to help with. I claim this is not Prompt Engineering. This is not to say that crafting good prompts is not a difficult task, but it does not involve doing any kind of sophisticated modifications to general "template" of a prompt.

Others, who I think do deserve to call themselves "Prompt Engineers" (and an awful lot more than that), have been writing about and utilizing the rich new eco-system

Sequelize: Step-By-Step

Sequelize is a powerful library in Javascript that makes it easy to manage a SQL database. Sequelize can layer over different protocols, but here we'll use PostgreSQL. At its core, Sequelize is an Object-Relational Mapper – meaning that it maps an object syntax onto our database schemas. Sequelize uses Node.JS and Javascript's object syntax to accomplish its mapping.

Under the hood, Sequelize used with PostgreSQL is several layers removed from our actual database:

First, we write our Sequelize, using Javascript objects to mimic the structure of our database tables.
Sequelize creates a SQL string and passes it to a lower-level library called pg (PostgreSQL).
pg connects to your PostgreSQL database and queries it or transforms its data.
pg passes the data back to Sequelize, which parses and returns that data as a Javascript object.

Aligning images

This is a guide for aligning images.

See the full Advanced Markdown doc for more tips and tricks

`left` alignment

Initialize

gitflow	git
`git flow init`	`git init`
	`git commit --allow-empty -m "Initial commit"`
	`git checkout -b develop master`

	"""Recommendation module.

	This module deals with using lightFM models in production and includes a
	LightFm subclass which provides a predict_online method to use in API or
	similar scenarios.
	"""
	import operator
	from logging import getLogger

	import numpy as np

	import React from 'react'
	import {compose, lifecycle} from 'recompose'
	import PropTypes from 'prop-types'

	import WaveSurfer from 'wavesurfer.js'

	const EVENTS = [
	'audioprocess',
	'error',
	'finish',

	<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
	"http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
	<html xmlns="http://www.w3.org/1999/xhtml">
	<head>
	<title>ScanCalc</title>
	<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
	<style type="text/css">
	table { border-collapse:collapse; }
	th { background-color: #ccc; }
	th,td { padding:3px; }