diff --git a/Chatbot.py b/Chatbot.py
index e24115a..4bc1423 100644
--- a/Chatbot.py
+++ b/Chatbot.py
@@ -14,7 +14,7 @@ class Chatbot(object):
 			np.load('markov_dict.npy')
 		except FileNotFoundError:
 			#If there's no initial file, make a new one:
-			initFile = {'Hoi':{self.END_OF_MESSAGE : 1}}
+			initFile = {'pairs':{'Hoi':{self.END_OF_MESSAGE : 1}}, 'trairs':{}}
 			np.save("markov_dict.npy", initFile)
 		except:
 			print("ERROR! - Could not open or create Markov chat file")
@@ -28,25 +28,45 @@ class Chatbot(object):
 		for i in range(len(corpus)-1):
 			yield (corpus[i], corpus[i+1])
 		
+	#Make word trairs. Yes, I completely made up "trair"
+	#It's like pairs, but with three, but not really, because there are two. OK?
+	#[1 0 1]
+	def makeTrairs(self, corpus):
+		for i in range(1, len(corpus)-1):
+			yield (corpus[i-1], corpus[i+1])
+		
 	#Learn from a human message
 	#Returns an updated dict
-	def learnFrom(self, initialWordDict, message: str):
-		newWordDict = initialWordDict
+	def learnFrom(self, wordDict, message: str):
+		newPairDict = wordDict['pairs']
+		newTrairDict = wordDict['trairs']
 		
 		message = message + " " + self.END_OF_MESSAGE
 		corpus = message.split()
 		pairs = self.makePairs(corpus)
+		trairs = self.makeTrairs(corpus)
+		print(trairs)
 
-		#Add wordcounts to dict
+		#Add wordcounts to pair dict
 		for word_1, word_2 in pairs:
-			if word_1 in newWordDict.keys():
-				if word_2 in newWordDict[word_1].keys():
-					newWordDict[word_1][word_2] += 1
+			if word_1 in newPairDict.keys():
+				if word_2 in newPairDict[word_1].keys():
+					newPairDict[word_1][word_2] += 1
 				else:
-					newWordDict[word_1][word_2] = 1
+					newPairDict[word_1][word_2] = 1
 			else:
-				newWordDict[word_1] = {word_2 : 1}
-		return newWordDict;
+				newPairDict[word_1] = {word_2 : 1}		
+		#Add wordcounts to trair dict
+		for word_1, word_3 in trairs:
+			if word_1 in newTrairDict.keys():
+				if word_3 in newTrairDict[word_1].keys():
+					newTrairDict[word_1][word_3] += 1
+				else:
+					newTrairDict[word_1][word_3] = 1
+			else:
+				newTrairDict[word_1] = {word_3 : 1}
+				
+		return {'pairs':newPairDict, 'trairs':newTrairDict};
 				
 	@commands.command(pass_context=True, hidden=True)
 	@asyncio.coroutine	
@@ -91,6 +111,8 @@ class Chatbot(object):
 		
 		#Get dictionary/Markov chain
 		wordDict = np.load('markov_dict.npy').item()
+		wordPairDict = wordDict['pairs']
+		wordTrairDict = wordDict['trairs']
 		#TODO store in global variable to minimise IO?
 
 		#Delete command
@@ -103,19 +125,29 @@ class Chatbot(object):
 		
 		#Pick a random first word (Which is not an end-of-message)
 		#TODO pick a word which responds to the previous message in the channel
-		first_word = np.random.choice(list(wordDict.keys()))
+		first_word = np.random.choice(list(wordPairDict.keys()))
 		while first_word == self.END_OF_MESSAGE: #or first_word.islower() ???
-			first_word = np.random.choice(list(wordDict.keys()))
+			first_word = np.random.choice(list(wordPairDict.keys()))
 		chain = [first_word]
 
 		#Go through the chain:
-		for i in range(MAX_N_WORDS):
+		for i in range(1, MAX_N_WORDS):
 			if chain[-1] == self.END_OF_MESSAGE:
 				break
+			
+			#Find the possibilities:
+			pairPossibilities = wordPairDict[chain[-1]].copy()
+			if i > 1: #we can only compare trairs if we have at least two words already
+				if chain[-2] in wordTrairDict:
+					trairPossibilities = wordTrairDict[chain[-2]].copy()
+					#If a trair matches, increase the frequency of the pair
+					for word2 in pairPossibilities.keys():
+						if word2 in trairPossibilities.keys():
+							pairPossibilities[word2] += trairPossibilities[word2]
 			#Calculate probabilities and pick a next word:
 			#https://stackoverflow.com/questions/835092/python-dictionary-are-keys-and-values-always-the-same-order
-			possible_words = list(wordDict[chain[-1]].keys())
-			weights = np.array(list(wordDict[chain[-1]].values()))
+			possible_words = list(pairPossibilities.keys())
+			weights = np.array(list(pairPossibilities.values()))
 			probs = weights / weights.sum()
 			chain.append(np.random.choice(possible_words, p=probs))